72 results
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2. Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research
- Author
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Polyzos, Efstathios and Siriopoulos, Costas
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- 2024
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3. The Devil is in the Details: On the Robust Determinants of Development Aid in G5 Sahel Countries
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Bayale, Nimonka and Kouassi, Brigitte Kanga
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- 2022
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4. Model Error (or Ambiguity) and Its Estimation, with Particular Application to Loss Reserving.
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Taylor, Greg and McGuire, Gráinne
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INSURANCE reserves ,ADMISSIBLE sets ,AMBIGUITY - Abstract
This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and likelihood of observed data. A posterior on the model set, conditional on the data, may then be calculated. An estimate of model error (for a loss reserve estimate) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be "thinner" than desired, and bootstrapping of the LASSO is used to increase this population. This also provides the bonus of an estimate of parameter error. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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5. Bayesian ensemble methods for predicting ground deformation due to tunnelling with sparse monitoring data.
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Zilong Zhang, Tingting Zhang, Xiaozhou Li, and Daniel Dias
- Subjects
DEFORMATIONS (Mechanics) ,TUNNEL design & construction ,BAYESIAN analysis ,STRUCTURAL health monitoring ,PREDICTION models - Abstract
Numerous analytical models have been developed to predict ground deformations induced by tunneling, which is a critical issue in tunnel engineering. However, the accuracy of these predictions is often limited by errors and uncertainties resulting from model selection and parameter fittings, given the paucity of monitoring data in field settings. This paper proposes a novel approach to estimate tunnelling-induced ground deformations by applying Bayesian model averaging to several representative prediction models. By accounting for both model and parameter uncertainties, this approach enables more realistic predictions of ground deformations than individual models. Specifically, our results indicate that the Gonzalez-Sagaseta model outperforms other models in predicting ground surface settlements, while the Loganathan-Poulos model is most suitable for predicting subsurface vertical and horizontal deformations. Importantly, our analysis reveals that when monitoring data are sparse, model uncertainties may contribute up to 78.7% of the total uncertainties. Thus, obtaining sufficient data for parameter fitting is crucial for accurate predictions. The proposed method in this study offers a more realistic and efficient prediction of tunnelling-induced ground deformations. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. Data-driven ensemble model for probabilistic prediction of debris-flow volume using Bayesian model averaging.
- Author
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Tian, Mi, Fan, Hao, Xiong, Zimin, and Li, Lihua
- Abstract
Accurate and reliable predictions of the debris-flow volume are the necessary prerequisite for potential hazard delineation and risk assessment of debris flows. Various theoretical, empirical, and machine learning methods have been proposed by researchers to estimate the debris-flow volume. However, current methods generally provide point-value deterministic predictions and have limitation in assessing the predictive uncertainties associated with the observation data, model parameters, and structures. This paper proposed a data-driven ensemble model to probabilistically forecast the debris-flow volume using multiple deterministic machine learning methods and Bayesian model averaging (BMA). The rainfall-induced debris flows in Taiwan were selected as an illustrative example to evaluate the feasibility of the proposed approach. Firstly, the debris-flow datasets are preprocessed by the principal component analysis (PCA) to select input variables. Then, four data-driven models are applied to provide deterministic estimates for ensemble forecasts. Finally, BMA incorporates the deterministic predictions of multiple data-driven models to generate probabilistic forecasts. The performances of individual data-driven models and BMA ensemble forecast are evaluated and compared. Results show that the proposed BMA ensemble model performs better than the single models for predicting the debris-flow volume in terms of the effectiveness and robustness. Ensemble models with good performance can combine the strengths of different models to improve the prediction accuracy. Weighting only good members may not achieve the best performance for both calibration and validation periods. The performance of different combinations of data-driven models is closely related to the observation data and the prediction accuracy of each model. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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7. Forecasting Bitcoin Futures: A Lasso-BMA Two-Step Predictor Selection for Investment and Hedging Strategies.
- Author
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Huang, Weige and Gao, Xiang
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BITCOIN ,HEDGING (Finance) ,BAYESIAN analysis ,MARKET volatility - Abstract
After Bitcoin futures were introduced by the Chicago Mercantile Exchange in December 2017, their trading volume has stayed in an uptrend due to speculation, though the scale is still small compared to other traditional futures. As increasing trading indicates more attention and the presence of institutional traders, there exists a need for reliable return and variance forecasts of Bitcoin futures contracts. Therefore, this paper first applies LASSO to pick out best-fitting predictors by shrinking the dimension of a universe of potential determinants sourced from intraday Bitcoin spot trades and daily futures variables. Then, a second round of predictor selection is conducted via Bayesian model averaging so that the modeling uncertainty can be mitigated. We find that factors standing out from this two-step procedure possess a strong predictive power for Bitcoin futures return and volatility in different time horizons. It is further demonstrated that the investment and hedging strategies established based on our forecasts perform well in out-of-sample validations. [ABSTRACT FROM AUTHOR]
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- 2023
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8. Bayesian Model Averaging to Account for Model Uncertainty in Estimates of a Vaccine's Effectiveness.
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Oliveira, Carlos R, Shapiro, Eugene D, and Weinberger, Daniel M
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VACCINE effectiveness ,LYME disease ,PARAMETER estimation ,CONFIDENCE intervals ,MEDICAL records ,CONFOUNDING variables - Abstract
Purpose: Vaccine effectiveness (VE) studies are often conducted after the introduction of new vaccines to ensure they provide protection in real-world settings. Control of confounding is often needed during the analyses, which is most efficiently done through multivariable modeling. When many confounders are being considered, it can be challenging to know which variables need to be included in the final model. We propose an intuitive Bayesian model averaging (BMA) framework for this task. Patients and Methods: Data were used from a matched case–control study that aimed to assess the effectiveness of the Lyme vaccine post-licensure. Cases were residents of Connecticut, 15– 70 years of age with confirmed Lyme disease. Up to 2 healthy controls were matched to each case subject by age. All participants were interviewed, and medical records were reviewed to ascertain immunization history and evaluate potential confounders. BMA was used to systematically search for potential models and calculate the weighted average VE estimate from the top subset of models. The performance of BMA was compared to three traditional single-best-model-selection methods: two-stage selection, stepwise elimination, and the leaps and bounds algorithm. Results: The analysis included 358 cases and 554 matched controls. VE ranged between 56% and 73% and 95% confidence intervals crossed zero in < 5% of all candidate models. Averaging across the top 15 models, the BMA VE was 69% (95% CI: 18– 88%). The two-stage, stepwise, and leaps and bounds algorithm yielded VE of 71% (95% CI: 21– 90%), 73% (95% CI: 26– 90%), and 74% (95% CI: 27– 91%), respectively. Conclusion: This paper highlights how the BMA framework can be used to generate transparent and robust estimates of VE. The BMA-derived VE and confidence intervals were similar to those estimated using traditional methods. However, by incorporating model uncertainty into the parameter estimation, BMA can lend additional rigor and credibility to a well-designed study. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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9. Inference and model determination for temperature-driven non-linear ecological models.
- Author
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Kondakis, Marios, Demiris, Nikolaos, Ntzoufras, Ioannis, and Papanikolaou, Nikos E.
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ECOLOGICAL models ,TEMPERATURE effect ,ARTHROPODA - Abstract
This paper is concerned with a contemporary Bayesian approach to the effect of temperature on developmental rates. We develop statistical methods using recent computational tools to model four commonly used ecological non-linear mathematical curves that describe arthropods' developmental rates. Such models address the effect of temperature fluctuations on the developmental rate of arthropods. In addition to the widely used Gaussian distributional assumption, we also explore Inverse Gamma-based alternatives, which naturally accommodate adaptive variance fluctuation with temperature. Moreover, to overcome the associated parameter indeterminacy in the case of no development, we suggest the zero-inflated Inverse Gamma model. The ecological models are compared graphically via posterior predictive plots and quantitatively via marginal likelihood estimates and Information criteria. Inference is performed using the Stan software and we investigate the statistical and computational efficiency of its Hamiltonian Monte Carlo and Variational Inference methods. We also explore model uncertainty and employ Bayesian Model Averaging framework for robust estimation of the key ecological parameters. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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10. Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure.
- Author
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Pacifico, Antonio
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STRUCTURAL panels ,MARKOV processes ,REGRESSION analysis ,VOLATILITY (Securities) ,MARKET volatility ,FORECASTING - Abstract
This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspecificity involved in compressed regression models. Methodologically, a multicountry large structural Panel Vector Autoregression is compressed through a robust model averaging to select the best subset across all possible combinations of predictors, where robust stands for the use of mixtures of proper conjugate priors. Concerning dynamic analysis, volatility changes and conditional density forecasts are addressed ensuring accurate predictive performance and capability. An empirical and simulated experiment are developed to highlight and discuss the functioning of the estimating procedure and forecasting accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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11. Combining dimensionality reduction methods with neural networks for realized volatility forecasting
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Bucci, Andrea, He, Lidan, and Liu, Zhi
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- 2023
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12. Modeling the spread of COVID‐19 in New York City.
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Olmo, Jose and Sanso‐Navarro, Marcos
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COVID-19 , *COVID-19 pandemic , *POISSON regression , *ZIP codes , *SOCIOECONOMIC factors - Abstract
This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID‐19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability in‐sample and out‐of‐sample. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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13. When Does Monetary Policy Sway House Prices? A Meta-Analysis
- Author
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Ehrenbergerova, Dominika, Bajzik, Josef, and Havranek, Tomas
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- 2023
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14. Explainable AI-based innovative hybrid ensemble model for intrusion detection.
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Ahmed, Usman, Jiangbin, Zheng, Almogren, Ahmad, Khan, Sheharyar, Sadiq, Muhammad Tariq, Altameem, Ayman, and Rehman, Ateeq Ur
- Abstract
Cybersecurity threats have become more worldly, demanding advanced detection mechanisms with the exponential growth in digital data and network services. Intrusion Detection Systems (IDSs) are crucial in identifying illegitimate access or anomalous behaviour within computer network systems, consequently opposing sensitive information. Traditional IDS approaches often struggle with high false positive rates and the ability to adapt embryonic attack patterns. This work asserts a novel Hybrid Adaptive Ensemble for Intrusion Detection (HAEnID), an innovative and powerful method to enhance intrusion detection, different from the conventional techniques. HAEnID is composed of a string of multi-layered ensemble, which consists of a Stacking Ensemble (SEM), a Bayesian Model Averaging (BMA), and a Conditional Ensemble method (CEM). HAEnID combines the best of these three ensemble techniques for ultimate success in detection with a considerable cut in false alarms. A key feature of HAEnID is an adaptive mechanism that allows ensemble components to change over time as network traffic patterns vary and new threats appear. This way, HAEnID would provide adequate protection as attack vectors change. Furthermore, the model would become more interpretable and explainable using Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). The proposed Ensemble model for intrusion detection on CIC-IDS 2017 achieves excellent accuracy (97-98%), demonstrating effectiveness and consistency across various configurations. Feature selection further enhances performance, with BMA-M (20) reaching 98.79% accuracy. These results highlight the potential of the ensemble model for accurate and reliable intrusion detection and, hence, is a state-of-the-art choice for accuracy and explainability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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15. Bayesian Model Averaging and Regularized Regression as Methods for Data-Driven Model Exploration, with Practical Considerations.
- Author
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Han, Hyemin
- Subjects
TEACHER researchers ,RESEARCH personnel ,QUANTITATIVE research ,PREDICTION models ,EXPERTISE - Abstract
Methodological experts suggest that psychological and educational researchers should employ appropriate methods for data-driven model exploration, such as Bayesian Model Averaging and regularized regression, instead of conventional hypothesis-driven testing, if they want to explore the best prediction model. I intend to discuss practical considerations regarding data-driven methods for end-user researchers without sufficient expertise in quantitative methods. I tested three data-driven methods, i.e., Bayesian Model Averaging, LASSO as a form of regularized regression, and stepwise regression, with datasets in psychology and education. I compared their performance in terms of cross-validity indicating robustness against overfitting across different conditions. I employed functionalities widely available via R with default settings to provide information relevant to end users without advanced statistical knowledge. The results demonstrated that LASSO showed the best performance and Bayesian Model Averaging outperformed stepwise regression when there were many candidate predictors to explore. Based on these findings, I discussed appropriately using the data-driven model exploration methods across different situations from laypeople's perspectives. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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16. Bootstrap Approximation of Model Selection Probabilities for Multimodel Inference Frameworks.
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Dajles, Andres and Cavanaugh, Joseph
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STATISTICAL bootstrapping ,AKAIKE information criterion ,PROBABILITY theory ,SELECTION bias (Statistics) ,DISTRIBUTION (Probability theory) - Abstract
Most statistical modeling applications involve the consideration of a candidate collection of models based on various sets of explanatory variables. The candidate models may also differ in terms of the structural formulations for the systematic component and the posited probability distributions for the random component. A common practice is to use an information criterion to select a model from the collection that provides an optimal balance between fidelity to the data and parsimony. The analyst then typically proceeds as if the chosen model was the only model ever considered. However, such a practice fails to account for the variability inherent in the model selection process, which can lead to inappropriate inferential results and conclusions. In recent years, inferential methods have been proposed for multimodel frameworks that attempt to provide an appropriate accounting of modeling uncertainty. In the frequentist paradigm, such methods should ideally involve model selection probabilities, i.e., the relative frequencies of selection for each candidate model based on repeated sampling. Model selection probabilities can be conveniently approximated through bootstrapping. When the Akaike information criterion is employed, Akaike weights are also commonly used as a surrogate for selection probabilities. In this work, we show that the conventional bootstrap approach for approximating model selection probabilities is impacted by bias. We propose a simple correction to adjust for this bias. We also argue that Akaike weights do not provide adequate approximations for selection probabilities, although they do provide a crude gauge of model plausibility. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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17. Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques.
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Chu, Ya, Song, Weifeng, and Chen, Dongbin
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MACHINE learning ,HAZARD mitigation ,FLOOD warning systems ,EMERGENCY management ,IDENTIFICATION ,RANDOM forest algorithms ,ECONOMIC security - Abstract
Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on historical mountain torrent hazards, a mountain torrent hazard prediction model was established by using Bayesian Model Average (BMA) and three classic machine learning algorithms (gradient-boosted decision tree (GBDT), backpropagation neural network (BP), and random forest (RF)). The mountain torrent hazard condition factors used in modeling were distance to river, elevation, precipitation, slope, gross domestic product (GDP), population, and land use type. Based on the proposed BMA model, flood risk maps were produced using GIS. The results demonstrated that the BMA model significantly improved upon the accuracy and stability of single models in identifying mountain torrent hazards. The F1-values (comprehensively displays the Precision and Recall) of the BMA model under three sets of test samples at different locations were 3.31–24.61% higher than those of single models. The risk assessment results of mountain torrents found that high-risk areas were mainly concentrated in the northern border and southern valleys of Yuanyang County, China. In addition, the feature importance analysis result demonstrated that distance to river and elevation were the most important factors affecting mountain torrent hazards. The construction of projects in mountainous areas should be as far away from rivers and low-lying areas as possible. The results of this study can provide a scientific basis for improving the identification methods of mountain torrent hazards and assisting decision-makers in the implementation of appropriate measures for mountain torrent hazard prevention and reduction. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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18. Stock price index analysis of four OPEC members: a Bayesian approach.
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Hatamerad, Saman, Asgharpur, Hossain, Adrangi, Bahram, and Haghighat, Jafar
- Abstract
This study examines the relationship between macroeconomic variables and stock price indices of four prominent OPEC oil-exporting members. Bayesian model averaging (BMA) and regularized linear regression (RLR) are employed to address uncertainties arising from different estimation models and variable selection. Jointness is utilized to determine the nature of relationships among variable pairs. The case study spans macroeconomic variables and stock prices from 1996 to 2018. BMA findings reveal a strong positive association between stock price indices and both consumer price index (CPI) and broad money growth in each analyzed OPEC country. Additionally, the study suggests a weak negative correlation between OPEC oil prices and the stock price index. RLR results align with BMA analysis, offering insights valuable for policymakers and international wealth managers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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19. Integrating Hydrological and Machine Learning Models for Enhanced Streamflow Forecasting via Bayesian Model Averaging in a Hydro-Dominant Power System.
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Torres, Francisca Lanai Ribeiro, Lima, Luana Medeiros Marangon, Reboita, Michelle Simões, de Queiroz, Anderson Rodrigo, and Lima, José Wanderley Marangon
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MACHINE learning ,STREAMFLOW ,FORECASTING ,DECISION making ,DISTRIBUTION (Probability theory) ,WATER demand management - Abstract
Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite their sophistication, face various uncertainties affecting their performance. These uncertainties can significantly influence both short-term and long-term operational planning in hydropower systems. To mitigate these effects, this study introduces a novel Bayesian model averaging (BMA) framework to improve the accuracy of streamflow forecasts in real hydro-dominant power systems. Designed to serve as an operational tool, the proposed framework incorporates predictive uncertainty into the forecasting process, enhancing the robustness and reliability of predictions. BMA statistically combines multiple models based on their posterior probability distributions, producing forecasts from the weighted averages of predictions. This approach updates weights periodically using recent historical data of forecasted and measured streamflows. Tested on inflows to 139 reservoirs and hydropower plants in Brazil, the proposed BMA framework proved to be more skillful than individual models, showing improvements in forecasting accuracy, especially in the South and Southeast regions of Brazil. This method offers a more reliable tool for streamflow prediction, enhancing decision making in hydropower system operations. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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20. Analyzing Parking Demand Characteristics Using a Bayesian Model Averaging.
- Author
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Liu, Bo, Zhang, Peng, Wu, Shubo, Zou, Yajie, Li, Linbo, and Tang, Shuning
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PARKING facilities ,DISTRIBUTION (Probability theory) ,PARK management ,FACILITY management ,LOGNORMAL distribution ,PARK design ,PARKING lots - Abstract
Parking duration analysis is an important aspect of evaluating parking demand. Identifying accurate distribution characteristics of parking duration can not only enhance parking efficiency and parking facility planning, but also provide essential support for parking delicacy management. Previous studies have proposed various statistical distributions to depict parking duration data. However, it is difficult to find a certain type of distribution to describe the characteristics of parking duration in diverse parking facilities, since model uncertainty is caused by stochastic parking behaviors and diverse parking environments. To address the model uncertainty, a Bayesian model averaging (BMA) was applied to integrate the advantages of different statistical distributions to depict parking duration characteristics. The parking dataset was collected from a commercial parking lot in Chengdu, China, and the dataset was categorized into two groups (i.e., temporary users and long-term users) to analyze. A set of statistical distributions was chosen as candidate models, and their corresponding unknown parameters were estimated. The posterior model probability for each candidate model was calculated according to the goodness-of-fit (GOF) metric. The findings of the study illustrate that there is no universally applicable distribution form (e.g., log-normal distribution) to depict the parking duration distribution for both user types, whereas the BMA approach assigns weights to candidate models and always provides an accurate description of the parking duration characteristics. The parking duration analysis is useful for improving parking management strategies and optimizing parking pricing policies. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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21. Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity.
- Author
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Kasim, Adetayo, Bean, Nathan, Hendriksen, Sarah Jo, Tai-Tsang Chen, Helen Zhou, and Psioda, Matthew A.
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BASKETS ,TRIAL practice ,SIGNAL detection ,COMPARATIVE studies ,INFERENTIAL statistics - Abstract
Background: Basket trials are increasingly used in oncology drug development for early signal detection, accelerated tumor-agnostic approvals, and prioritization of promising tumor types in selected patients with the same mutation or biomarker. Participants are grouped into so-called baskets according to tumor type, allowing investigators to identify tumors with promising responses to treatment for further study. However, it remains a question as to whether and how much the adoption of basket trial designs in oncology have translated into patient benefits, increased pace and scale of clinical development, and de-risking of downstream confirmatory trials. Methods: Innovation in basket trial design and analysis includes methods that borrow information across tumor types to increase the quality of statistical inference within each tumor type. We build on the existing systematic reviews of basket trials in oncology to discuss the current practices and landscape. We conceptually illustrate recent innovative methods for basket trials, with application to actual data from recently completed basket trials. We explore and discuss the extent to which innovative basket trials can be used to de-risk future trials through their ability to aid prioritization of promising tumor types for subsequent clinical development. Results: We found increasing adoption of basket trial design in oncology, but largely in the design of single-arm phase II trials with a very low adoption of innovative statistical methods. Furthermore, the current practice of basket trial design, which does not consider its impact on the clinical development plan, may lead to a missed opportunity in improving the probability of success of a future trial. Gating phase II with a phase Ib basket trial reduced the size of phase II trials, and losses in the probability of success as a result of not using innovative methods may not be recoverable by running a larger phase II trial. Conclusion: Innovative basket trial methods can reduce the size of early phase clinical trials, with sustained improvement in the probability of success of the clinical development plan. We need to do more as a community to improve the adoption of these methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
22. Probabilistic post-processing of short to medium range temperature forecasts: Implications for heatwave prediction in India
- Author
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Saminathan, Sakila and Mitra, Subhasis
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- 2024
- Full Text
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23. Linking Pensions to Life Expectancy: Tackling Conceptual Uncertainty through Bayesian Model Averaging.
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Bravo, Jorge M. and Ayuso, Mercedes
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LIFE expectancy ,PENSIONS ,PENSION reform ,COST control ,RETIREMENT age ,OLDER people - Abstract
Linking pensions to longevity developments at retirement age has been one of the most common policy responses of pension schemes to aging populations. The introduction of automatic stabilizers is primarily motivated by cost containment objectives, but there are other dimensions of welfare restructuring in the politics of pension reforms, including recalibration, rationalization, and blame avoidance for unpopular policies that involve retrenchments. This paper examines the policy designs and implications of linking entry pensions to life expectancy developments through sustainability factors or life expectancy coefficients in Finland, Portugal, and Spain. To address conceptual and specification uncertainty in policymaking, we propose and apply a Bayesian model averaging approach to stochastic mortality modeling and life expectancy computation. The results show that: (i) sustainability factors will generate substantial pension entitlement reductions in the three countries analyzed; (ii) the magnitude of the pension losses depends on the factor design; (iii) to offset pension cuts and safeguard pension adequacy, individuals will have to prolong their working lives significantly; (iv) factor designs considering cohort longevity markers would have generated higher pension cuts in countries with increasing life expectancy gap. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
24. An integrative data-driven approach for monitoring corn biomass under irrigation water and nitrogen levels based on UAV-based imagery.
- Author
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Feizolahpour, Farid, Besharat, Sina, Feizizadeh, Bakhtiar, Rezaverdinejad, Vahid, and Hessari, Behzad
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IRRIGATION water ,WATER levels ,NITROGEN in water ,CROP yields ,BIOMASS estimation ,CORN ,CORN stover - Abstract
Unmanned aerial vehicle (UAV)-based remote sensing has been widely considered recently in field scale crop yield estimation. In this research, the capability of 13 spectral indices in the form of 5 groups was studied under different irrigation water and N fertilizer managements in terms of corn biomass monitoring and estimation. Farm experiments were conducted at Urmia University, Iran. The research was done using a randomized complete block design at three levels of 60, 80, and 100% of irrigation water and nitrogen requirements during four replications. The aerial imagery operations were performed using a fixed-wing UAV equipped with a Sequoia sensor during three plant growth stages including stem elongation, flowering, and silking. The effect of different irrigation water and nitrogen levels on vegetation indices and crop biomass was examined using variance decomposition analysis. Then, the correlation of the vegetation indices with corn biomass was evaluated by fitting linear regression models. Based on the obtained results, the indices based on near infrared (NIR) and red-edge spectral bands showed a better performance. Thus, the MERIS terrestrial chlorophyll index (MTCI) indicated the highest accuracy at estimating corn biomass during the growing season with the R
2 and RMSE values of 0.92 and 8.27 ton/ha, respectively. Finally, some Bayesian model averaging (BMA) models were proposed to estimate corn biomass based on the selected indices and different spectral bands. Results of the BMA models revealed that the accuracy of biomass estimation models could be improved using the capabilities and advantages of different vegetation indices. [ABSTRACT FROM AUTHOR]- Published
- 2023
- Full Text
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25. Diurnal Characteristics of Heavy Precipitation Events under Different Synoptic Circulation Patterns in the Middle and Lower Reaches of the Yangtze River in Summer.
- Author
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Qi, Haixia, Lin, Chunze, Peng, Tao, Zhi, Xiefei, Cui, Chunguang, Chen, Wen, Yin, Zhiyuan, Shen, Tieyuan, and Xiang, Yiheng
- Subjects
RAINSTORMS ,TYPHOONS ,GEOPOTENTIAL height ,SUMMER - Abstract
Aiming at the rainstorm days (≥50 mm/d) in the middle and lower reaches of the Yangtze River during 2010–2020, the obliquely rotated principal component in T-mode (PCT) method is used to classify the daily mean 850 hPa geopotential height, including Type 1 (vortex/shear line), Type 2 (frontal surface), Type 3 (warm shear line), Type 4 (warm inverse trough line), Type 5 (typhoon-westerly trough), and Type 6 (easterly wave). We studied the weather system configurations of different synoptic circulation patterns, their long-term trends, and their impacts on diurnal variations of heavy precipitation and drew the following conclusions: Type 1, Type 2, or Type 3 shows balanced double-peak frequencies of the start time of heavy precipitation during 06:00–08:00 BT and around 17:00 BT, respectively. For Type 1, dynamical lifting and thermal lifting play balanced roles, while for Type 2 and Type 3, dynamical lifting plays a key role. The number of rainstorm stations for Type 1 shows a slight increasing trend, while that for Type 2 or Type 3 shows a significant increasing trend. Type 4, Type 5, or Type 6 show a significant single peak frequency of the start time during 15:00–16:00. Type 5 and Type 6 are mainly affected by dynamical lifting along with favorable cape values, which can trigger rainstorms. The number of rainstorm stations for Type 4 or Type 6 shows a decreasing trend (that for Type 4 is more significant), while that for Type 5 shows a slightly increasing trend. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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26. A Comparison of Bayesian Multivariate Versus Univariate Normal Regression Models for Prediction.
- Author
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Li, Xun, Ghosh, Joyee, and Villarini, Gabriele
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REGRESSION analysis ,PREDICTION models ,OCEAN temperature ,CLIMATOLOGY ,HURRICANES ,MARKOV chain Monte Carlo - Abstract
In many moderate dimensional applications we have multiple response variables that are associated with a common set of predictors. When the main objective is prediction of the response variables, a natural question is: do multivariate regression models that accommodate dependency among the response variables improve prediction compared to their univariate counterparts? Note that in this article, by univariate versus multivariate regression models we refer to regression models with a single versus multiple response variables, respectively. We assume that under both scenarios, there are multiple covariates. Our question is motivated by an application in climate science, which involves the prediction of multiple metrics that measure the activity, intensity, severity etc. of a hurricane season. Average sea surface temperatures (SSTs) during the hurricane season have been used as predictors for each of these metrics, in separate univariate regression models, in the literature. Since the true SSTs are yet to be observed during prediction, typically their forecasts from multiple climate models are used as predictors. Some climate models have a few missing values so we develop Bayesian univariate/multivariate normal regression models, that can handle missing covariates and variable selection uncertainty. Whether Bayesian multivariate normal regression models improve prediction compared to their univariate counterparts is not clear from the existing literature, and in this work we try to fill this gap. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
27. Stochastic analysis of steel frames considering the material, geometrical and loading uncertainties.
- Author
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Dang, Huy-Khanh, Thai, Duc-Kien, and Kim, Seung-Eock
- Subjects
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STEEL framing , *STEEL analysis , *LATIN hypercube sampling , *STRUCTURAL steel , *STRUCTURAL frames , *YIELD stress , *STOCHASTIC analysis - Abstract
• An effective model of SPAAP predicts the accurate steel frame responses. • Influence of uncertain parameters is significantly on the structural steel design. • The yield stress is the most sensitive comparing with the other uncertainties. • Dual mNR and GDC algorithms find the system deformation-based reliability index. • The BMA technique is considerable in predicting response of structural components. This paper develops a Stochastic Practical Advanced Analysis Program for stochastic analysis of structural steel frames. The second-order refined plastic-hinge analysis method combined with the technical simulation of Latin Hypercube Sampling is developed to predict the actual ultimate load-carrying capacity of steel frames and investigate the sensitivity of the uncertain input parameters. The input parameters of material properties, geometrical characteristics, and load combinations are considered as independent random variables that may occur in simultaneous randomness. A proposed parallel analytical technique integrates the modified Newton-Raphson and Generalized Displacement Control algorithms to solve the nonlinear inelastic problems to estimate the critical displacement-based system reliability index. The results of the statistical analysis in terms of coefficients of variation and Pearson correlation index show that the yield strength of material is the most sensitive with respect to the behavior of steel frames. The Bayesian Model Averaging is employed to find the most influential structural components on the ultimate structural resistance. The useful results of this research may be used in steel structure design and maintenance in practice. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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28. Bayesian negative binomial regression model with unobserved covariates for predicting the frequency of north atlantic tropical storms.
- Author
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Li, Xun, Ghosh, Joyee, and Villarini, Gabriele
- Subjects
TROPICAL storms ,REGRESSION analysis ,OCEAN temperature ,ATMOSPHERIC models ,INDEPENDENT variables ,TROPICAL cyclones - Abstract
Predicting the annual frequency of tropical storms is of interest because it can provide basic information towards improved preparation against these storms. Sea surface temperatures (SSTs) averaged over the hurricane season can predict annual tropical cyclone activity well. But predictions need to be made before the hurricane season when the predictors are not yet observed. Several climate models issue forecasts of the SSTs, which can be used instead. Such models use the forecasts of SSTs as surrogates for the true SSTs. We develop a Bayesian negative binomial regression model, which makes a distinction between the true SSTs and their forecasts, both of which are included in the model. For prediction, the true SSTs may be regarded as unobserved predictors and sampled from their posterior predictive distribution. We also have a small fraction of missing data for the SST forecasts from the climate models. Thus, we propose a model that can simultaneously handle missing predictors and variable selection uncertainty. If the main goal is prediction, an interesting question is should we include predictors in the model that are missing at the time of prediction? We attempt to answer this question and demonstrate that our model can provide gains in prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
29. Crime in Philadelphia: Bayesian Clustering with Particle Optimization.
- Author
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Balocchi, Cecilia, Deshpande, Sameer K., George, Edward I., and Jensen, Shane T.
- Subjects
CLUSTERING of particles ,SOCIAL boundaries ,CRIME statistics ,CITIES & towns ,CRIME ,CRIMINAL methods - Abstract
Accurate estimation of the change in crime over time is a critical first step toward better understanding of public safety in large urban environments. Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled "sharing of information" between spatially adjacent neighborhoods. Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in crime patterns. In this situation, standard prior choices often yield overly smooth parameter estimates, which can ultimately produce mis-calibrated forecasts. To prevent potential over-smoothing, we introduce a prior that partitions the set of neighborhoods into several clusters and encourages spatial smoothness within each cluster. In terms of model implementation, conventional stochastic search techniques are computationally prohibitive, as they must traverse a combinatorially vast space of partitions. We introduce an ensemble optimization procedure that simultaneously identifies several high probability partitions by solving one optimization problem using a new local search strategy. We then use the identified partitions to estimate crime trends in Philadelphia between 2006 and 2017. On simulated and real data, our proposed method demonstrates good estimation and partition selection performance. for this article are available online. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
30. Bayesian model averaging for predicting factors associated with length of COVID-19 hospitalization
- Author
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Bahrami, Shabnam, Hajian-Tilaki, Karimollah, Bayani, Masomeh, Chehrazi, Mohammad, Mohamadi-Pirouz, Zahra, and Amoozadeh, Abazar
- Published
- 2023
- Full Text
- View/download PDF
31. Context matters: The drivers of environmental concern in European regions.
- Author
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Peisker, Jonas
- Subjects
INCOME distribution ,ENVIRONMENTAL quality ,INCOME ,ENVIRONMENTAL policy ,HIGH-income countries - Abstract
• Environmental concern depends on socio-economic and environmental context. • Bayesian Model Averaging accounts for model specification as source of uncertainty. • Higher income level, lower inequality, and cleaner industry bolster concern. • Socio-economic and geographical covariates are more important than weather events. Environmental concern is crucial as bottom-up support for policies that aim to tackle the multiple ecological crises. This paper investigates which characteristics of 206 European regions are robust drivers of generalized environmental concern. To this end, 25 Eurobarometer survey waves between 2009 and 2019 were combined with measures of the regional economy, population, geography, environmental quality, and meteorological events. Bayesian model averaging is used to systematically account for model uncertainty in the estimation of partial correlations. The results indicate that environmental concern increases with income level, a more equal distribution of income and wealth, and a less greenhouse gas-intensive industrial sector. Furthermore, regions with younger and better educated populations exhibit higher levels of environmental concern. In terms of environmental characteristics, both geographical vulnerability to natural hazards and meteorological events affect environmental concern. The results highlight the importance of the socio-economic and environmental context of opinion formation and have implications for designing and communicating environmental policies. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
32. Medium Term Streamflow Prediction Based on Bayesian Model Averaging Using Multiple Machine Learning Models.
- Author
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He, Feifei, Zhang, Hairong, Wan, Qinjuan, Chen, Shu, and Yang, Yuqi
- Subjects
MACHINE learning ,RECURRENT neural networks ,STREAMFLOW ,BACK propagation ,HYDROLOGICAL forecasting ,SIMULATED annealing - Abstract
Medium-term hydrological streamflow forecasting can guide water dispatching departments to arrange the discharge and output plan of hydropower stations in advance, which is of great significance for improving the utilization of hydropower energy and has been a research hotspot in the field of hydrology. However, the distribution of water resources is uneven in time and space. It is important to predict streamflow in advance for the rational use of water resources. In this study, a Bayesian model average integrated prediction method is proposed, which combines artificial intelligence algorithms, including long-and short-term memory neural network (LSTM), gate recurrent unit neural network (GRU), recurrent neural network (RNN), back propagation (BP) neural network, multiple linear regression (MLR), random forest regression (RFR), AdaBoost regression (ABR) and support vector regression (SVR). In particular, the simulated annealing (SA) algorithm is used to optimize the hyperparameters of the model. The practical application of the proposed model in the ten-day scale inflow prediction of the Three Gorges Reservoir shows that the proposed model has good prediction performance; the Nash–Sutcliffe efficiency NSE is 0.876, and the correlation coefficient r is 0.936, which proves the accuracy of the model. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
33. Reproducible Model Selection Using Bagged Posteriors.
- Author
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Huggins, Jonathan H. and Miller, Jeffrey W.
- Subjects
BAYESIAN analysis ,MATHEMATICAL variables ,MATHEMATICAL optimization ,PROBABILITY theory ,REGRESSION analysis - Abstract
Bayesian model selection is premised on the assumption that the data are generated from one of the postulated models. However, in many applications, all of these models are incorrect (that is, there is misspecification). When the models are misspecified, two or more models can provide a nearly equally good fit to the data, in which case Bayesian model selection can be highly unstable, potentially leading to self-contradictory findings. To remedy this instability, we propose to use bagging on the posterior distribution ("BayesBag") - that is, to average the posterior model probabilities over many bootstrapped datasets. We provide theoretical results characterizing the asymptotic behavior of the posterior and the bagged posterior in the (misspecified) model selection setting. We empirically assess the BayesBag approach on synthetic and real-world data in (i) feature selection for linear regression and (ii) phylogenetic tree reconstruction. Our theory and experiments show that, when all models are misspecified, BayesBag (a) provides greater reproducibility and (b) places posterior mass on optimal models more reliably, compared to the usual Bayesian posterior; on the other hand, under correct specification, BayesBag is slightly more conservative than the usual posterior, in the sense that BayesBag posterior probabilities tend to be slightly farther from the extremes of zero and one. Overall, our results demonstrate that BayesBag provides an easy-to-use and widely applicable approach that improves upon Bayesian model selection by making it more stable and reproducible. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. Revisiting the Determinants of Consumption: A Bayesian Model Averaging Approach.
- Author
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Deniz, Pinar and Stengos, Thanasis
- Subjects
CONSUMPTION (Economics) - Abstract
This study revisits the widely researched area of the consumption function using Bayesian Model Averaging (BMA) for a panel of EU countries to deal with the uncertainty of potential determinants, using the convergence club analysis to construct homogeneous groups by income. BMA suggests that income is the only variable that is found to be a strong determinant across different country groups, whereas other variables have varying importance for different country groups. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. DETERMINANTS OF CROATIAN MERCHANDISE TRADE: BAYESIAN MODEL AVERAGING
- Author
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Mile Bošnjak, Ivan Novak, and Maja Bašić
- Subjects
međunarodna robna trgovina ,Bayesov model uprosječivanja ,Republika Hrvatska ,General Economics, Econometrics and Finance ,international merchandise trade ,Bayesian model averaging ,the Republic of Croatia - Abstract
Cilj ovog rada je empirijski provjeriti različite teorije međunarodne trgovine na primjeru međunarodne robne trgovine Republike Hrvatske. Na temeljima različitih teorijskih postavki definiran je analitički okvir kroz koji se vrednuje valjanost pojedinih teorija međunarodne trgovine u objašnjavanju obrazaca trgovine Republike Hrvatske. Razmatra se utjecaj geografske udaljenosti glavnih gradova, broj stanovnika starijih od 15 godina, ulaganje u istraživanje i razvoj, razina bruto domaćeg proizvoda, obilnost faktora proizvodnje te indeks financijske razvijenosti na robni izvoz i uvoz Republike Hrvatske. Na temelju broja potencijalnih nezavisnih varijabli i veličine uzorka istraživanja u ovom radu Bayesov model uprosječivanja nameće se kao primjeren empirijski pristup. Na uzorku podataka o međunarodnoj robnoj trgovini Republike Hrvatske s 26 zemalja partnerica u 2019. godini, empirijski rezultati su pokazali da su geografska udaljenost glavnih gradova i broj stanovnika starijih od 15 godina u zemlji partnerici glavne odrednice robnog uvoza i robnog izvoza Republike Hrvatske. Transportni troškovi su teorijski konzistentno značajna odrednica izvoza i uvoza Republike Hrvatske. Broj stanovnika starijih od 15 godina može se protumačiti dvojako. U zemlji partnerici kao odrednica robnog izvoza Republike Hrvatske predstavlja veličinu tržišta dok kao odrednica robnog uvoza Republike Hrvatske predstavlja raspoloživost radne snage u zemlji partnerici. Daljnja empirijska istraživanja mogu se usmjeriti na eksplicitnu provjeru ekonomije obujma kao odrednice međunarodne robne trgovine Republike Hrvatske., This paper aims to evaluate competing theoretical approaches to explain merchandise trade of the Republic of Croatia. Following analytical framework derived from competing theories of international trade Croatian merchandise trade pattern was examined. Paper considers the influence of geographical distance between major cities, number of citizens older than 15 years, investment in research and development, level of gross domestic product, production factor abundance and financial index development to export and import of Republic of Croatia. Regarding the number of potential independent variables and the sample size this paper applies Bayesian model averaging. Using data sample of international merchandise trade between the Republic of Croatia and 26 trading partners in 2019, empirical results pointed towards geographical distance and number of citizens older than 15 years as main drivers of international merchandise trade. Empirical findings were in line with theoretical assumption suggesting transport costs are relevant drivers of both Croatian merchandise imports and exports. Number of citizens can be interpreted ambiguously. Number of citizens can be seen as market size or labor supply. Economies of scales might potentially be a relevant theory to explain Republic of Croatia’s merchandise trade, while further research should be directed toward its empirical evaluation in case of the Republic of Croatia.
- Published
- 2022
36. Bayesian Model Averaging Ensemble Approach for Multi-Time-Ahead Groundwater Level Prediction Combining the GRACE, GLEAM, and GLDAS Data in Arid Areas.
- Author
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Zhou, Ting, Wen, Xiaohu, Feng, Qi, Yu, Haijiao, and Xi, Haiyang
- Subjects
WATER table ,GROUNDWATER management ,SUPPORT vector machines ,DECISION making ,RANDOM forest algorithms - Abstract
Accurate groundwater level (GWL) prediction is essential for the sustainable management of groundwater resources. However, the prediction of GWLs remains a challenge due to insufficient data and the complicated hydrogeological system. In this study, we investigated the ability of the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Evaporation Amsterdam Model (GLEAM) data, the Global Land Data Assimilation System (GLDAS) data, and the publicly available meteorological data in 1-, 2-, and 3-month-ahead GWL prediction using three traditional machine learning models (extreme learning machine, ELM; support vector machine, SVR; and random forest, RF). Meanwhile, we further developed the Bayesian model averaging (BMA) by combining the ELM, SVR, and RF models to avoid the uncertainty of the single models and to improve the predicting accuracy. The validity of the forcing data and the BMA model were assessed for three GWL monitoring wells in the Zhangye Basin in Northwest China. The results indicated that the applied forcing data could be treated as validated inputs to predict the GWL up to 3 months ahead due to the achieved high accuracy of the machine learning models (NS > 0.55). The BMA model could significantly improve the performance of the single machine learning models. Overall, the BMA model reduced the RMSE of the ELM, SVR, and RF models in the testing period by about 13.75%, 24.01%, and 17.69%, respectively; while it improved the NS by about 8.32%, 16.13%, and 9.67% for 1-, 2-, and 3-month-ahead GWL prediction, respectively. The uncertainty analysis results also verified the reliability of the BMA model in multi-time-ahead GWL predicting. This highlighted the efficiency of the satellite data, satellite-based data, and publicly available data as substitute inputs in machine-learning-based GWL prediction, particularly for areas with insufficient or missing data. Meanwhile, the BMA ensemble strategy can serve as a powerful and reliable approach in multi-time-ahead GWL prediction when risk-based decision making is needed or a lack of relevant hydrogeological data impedes the application of the physical models. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Computational Evidence for Laboratory Diagnostic Pathways: Extracting Predictive Analytes for Myocardial Ischemia from Routine Hospital Data.
- Author
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Liniger, Zara, Ellenberger, Benjamin, and Leichtle, Alexander Benedikt
- Subjects
MYOCARDIAL ischemia ,DATA augmentation ,TROPONIN ,PREDICTION models ,LABORATORIES - Abstract
Background: Laboratory parameters are critical parts of many diagnostic pathways, mortality scores, patient follow-ups, and overall patient care, and should therefore have underlying standardized, evidence-based recommendations. Currently, laboratory parameters and their significance are treated differently depending on expert opinions, clinical environment, and varying hospital guidelines. In our study, we aimed to demonstrate the capability of a set of algorithms to identify predictive analytes for a specific diagnosis. As an illustration of our proposed methodology, we examined the analytes associated with myocardial ischemia; it was a well-researched diagnosis and provides a substrate for comparison. We intend to present a toolset that will boost the evolution of evidence-based laboratory diagnostics and, therefore, improve patient care. Methods: The data we used consisted of preexisting, anonymized recordings from the emergency ward involving all patient cases with a measured value for troponin T. We used multiple imputation technique, orthogonal data augmentation, and Bayesian Model Averaging to create predictive models for myocardial ischemia. Each model incorporated different analytes as cofactors. In examining these models further, we could then conclude the predictive importance of each analyte in question. Results: The used algorithms extracted troponin T as a highly predictive analyte for myocardial ischemia. As this is a known relationship, we saw the predictive importance of troponin T as a proof of concept, suggesting a functioning method. Additionally, we could demonstrate the algorithm's capabilities to extract known risk factors of myocardial ischemia from the data. Conclusion: In this pilot study, we chose an assembly of algorithms to analyze the value of analytes in predicting myocardial ischemia. By providing reliable correlations between the analytes and the diagnosis of myocardial ischemia, we demonstrated the possibilities to create unbiased computational-based guidelines for laboratory diagnostics by using computational power in today's era of digitalization. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
38. Nowcasting of Service Sector by Using Traffic Counting Data in Iran
- Author
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Sajad Ebrahimi
- Subjects
service ,transportation ,neural network ,nowcasting ,bayesian model averaging ,Economic growth, development, planning ,HD72-88 - Abstract
Predictable and unpredictable delays in the national accounts data dissemination in Iran highlight the nowcasting of the economy’s state with using timely and high-frequency data. The large share of service sectors in GDP make forecasting of this sector more important. This paper seeks to answer the question of whether the status of the service and transportation sector can be predicted by using the vehicle traffic count dataset. In this regard, daily data on 2590 points of the country's roads from 2015 to September 2021 is used. In addition to using a simple aggregation method to construct the index, Artificial Neural Network model (ANNs) and Bayesian Model Averaging (BMA) are also used. The results show that the estimation indices extracted from these data have less forecast error than the benchmark models (ARMA) and can represent changes in both services and transportation sectors. The comparison of different methods of index construction shows the index extracted from Neural Network model has less error than other methods.
- Published
- 2022
- Full Text
- View/download PDF
39. Model Error (or Ambiguity) and Its Estimation, with Particular Application to Loss Reserving
- Author
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Greg Taylor and Gráinne McGuire
- Subjects
Bayesian model averaging ,bootstrap ,bootstrap matrix ,forecast error ,GLM ,internal model structure error ,Insurance ,HG8011-9999 - Abstract
This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is considered in two parts: one part that is capable of estimation from past data (internal model error), and another part that is not (external model error). Attention is focused here on internal model error. Estimation of this error component is approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set of admissible models, each with its prior probability and likelihood of observed data. A posterior on the model set, conditional on the data, may then be calculated. An estimate of model error (for a loss reserve estimate) is obtained as the variance of the loss reserve according to this posterior. The population of models entering materially into the support of the posterior may turn out to be “thinner” than desired, and bootstrapping of the LASSO is used to increase this population. This also provides the bonus of an estimate of parameter error. It turns out that the estimates of parameter and model errors are entangled, and dissociation of them is at least difficult, and possibly not even meaningful. These matters are discussed. The majority of the discussion applies to forecasting generally, but numerical illustration of the concepts is given in relation to insurance data and the problem of insurance loss reserving.
- Published
- 2023
- Full Text
- View/download PDF
40. Robust determinants of the shadow economy.
- Subjects
WASTE in government spending ,UNEMPLOYMENT ,UNEMPLOYMENT statistics ,INFORMAL sector ,GROWTH rate ,ECONOMIES of scale - Abstract
This study examines the determinants of the shadow economy by employing Bayesian Model Averaging technique, which allows taking into account model uncertainty. Having estimated millions of combinations of models, the study revealed that while higher GDP growth rate, trade openness, and better institutional quality lead to the reduction of the informal sector, higher rate of unemployment, complicated regulations, and the large size of government are associated with a greater size of the shadow economy. Lessening bureaucratic complexity by eliminating burdensome regulations could help to reduce both wasteful spending of government on operating these regulations and the size of the underground economy. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
41. Comparing methods for statistical inference with model uncertainty.
- Author
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Porwal, Anupreet and Raftery, Adrian E.
- Subjects
STATISTICAL models ,INFERENTIAL statistics ,MARKOV chain Monte Carlo ,REGRESSION analysis ,PARAMETER estimation - Abstract
Probability models are used for many statistical tasks, notably parameter estimation, interval estimation, inference about model parameters, point prediction, and interval prediction. Thus, choosing a statistical model and accounting for uncertainty about this choice are important parts of the scientific process. Here we focus on one such choice, that of variables to include in a linear regression model. Many methods have been proposed, including Bayesian and penalized likelihood methods, and it is unclear which one to use. We compared 21 of the most popular methods by carrying out an extensive set of simulation studies based closely on real datasets that span a range of situations encountered in practical data analysis. Three adaptive Bayesian model averaging (BMA) methods performed best across all statistical tasks. These used adaptive versions of Zellner's g-prior for the parameters, where the prior variance parameter g is a function of sample size or is estimated fromthe data. We found that forBMAmethods implemented with Markov chain Monte Carlo, 10,000 iterations were enough. Computationally, we found two of the three best methods (BMA with g = n and empirical Bayes-local) to be competitive with the least absolute shrinkage and selection operator (LASSO), which is often preferred as a variable selection technique because of its computational efficiency. BMA performed better than Bayesian model selection (in which just one model is selected). [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
42. Re-Evaluating the Relationship Between Economic Development and Self-Employment, at the Macro-Level: A Bayesian Model Averaging Approach.
- Author
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Rodriguez-Santiago, Ana
- Subjects
ECONOMIC development ,SELF-employment ,LABOR market ,ECONOMIC expansion ,LITERATURE reviews - Abstract
We re-evaluate the relationship between stages of economic development and entrepreneurship, at the macro level. We first conduct a literature review of previous empirical research on cross-country determinants of entrepreneurship in order to put our contribution in perspective. To circumvent problems related to model uncertainty we use Bayesian Model Averaging (BMA) to evaluate the robustness of determinants of economic growth in a new dataset of 117 countries in the 2005-2019 period, allowing fixed effects and investigating the existence of heterogeneity allowing interactions of our focus variable with other regressors. Our empirical analysis then shows that the variation of self-employment rates across countries are mainly determined by variations in the unemployment, the stage of economic development and the variations in labor market frictions. When interactions are taken into account, results confirm that there is a differential effect of labor market frictions in countries with different levels of income. Frictions in labor market may encourage becoming self-employed in richer countries. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
43. Individual discount rates: a meta-analysis of experimental evidence.
- Author
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Matousek, Jindrich, Havranek, Tomas, and Irsova, Zuzana
- Subjects
DISCOUNT prices ,PUBLICATION bias - Abstract
A key parameter estimated by lab and field experiments in economics is the individual discount rate—and the results vary widely. We examine the extent to which this variance can be attributed to observable differences in methods, subject pools, and potential publication bias. To address the model uncertainty inherent to such an exercise we employ Bayesian and frequentist model averaging. We obtain evidence consistent with publication bias against unintuitive results. The corrected mean annual discount rate is 0.33. Our findings also suggest that discount rates are independent across domains: people tend to be less patient when health is at stake compared to money. Negative framing is associated with more patience. Finally, the results of lab and field experiments differ systematically, and it also matters whether the experiment relies on students or uses broader samples of the population. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
44. A Bayesian-Model-Averaging Copula Method for Bivariate Hydrologic Correlation Analysis
- Author
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Yizhuo Wen, Aili Yang, Xiangming Kong, and Yueyu Su
- Subjects
archimedean copula ,Bayesian model averaging ,rainfall and runoff ,Xiangxi river watershed ,climate change ,Environmental sciences ,GE1-350 - Abstract
A Bayesian-model-averaging Copula (i.e., BMAC) approach was proposed for correlation analysis of monthly rainfall and runoff in Xiangxi River watershed, China. The BMAC approach was formulated by incorporating existing Bayesian model averaging (i.e., BMA) method and Archimedean Copula techniques (e.g., Gumbel-Hougaard, Clayton and Frank Copulas) within a general bivariate hydrologic correlation analysis framework. In this paper, the BMA method was applied to determine the marginal distribution functions of variables, and the Copula method was used to analyze the correlation. Results showed that: 1) the BMA method could improve the representation of the marginal distribution of hydrological variables with smaller corresponding errors; 2) the predictive joint distributions of monthly rainfall and runoff was much better calibrated by the Gumbel Copula according to criteria of the root mean square error (i.e., RMSE), Akaike Information Criterion (i.e., AIC) values, Anderson-Darling test (i.e., AD test), and Cramer-von Mises test (i.e., CM test); and 3) the bivariate joint probability and return periods of rainfall and runoff based on the optimal Copula function was characterized and the monthly rainfall and runoff presented a strong positive correlation based on Kendall and Spearman’s rank correlation coefficients. Therefore, the BMAC approach performed reasonably well and can be further used to simulate runoff values according to the historical and predicted rainfall data. Highlights: 1) A Bayesian-model-averaging Copula method is proposed for correlation analysis; 2) the monthly rainfall and runoff in Xiangxi River watershed has a positive correlation. 3) Gumbel Copula is the best in modelling the joint distributions in the Xiangxi River watershed.
- Published
- 2022
- Full Text
- View/download PDF
45. Bayesian estimation of the effect of health inequality in disease detection
- Author
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Lope, Dinah Jane, Demirhan, Haydar, and Dolgun, Anil
- Published
- 2022
- Full Text
- View/download PDF
46. Does Bayesian model averaging improve polynomial extrapolations? Two toy problems as tests.
- Author
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Connell, M A, Billig, I, and Phillips, D R
- Subjects
POLYNOMIALS ,PARAMETER estimation ,GENERATING functions ,TOYS ,EXTRAPOLATION - Abstract
We assess the accuracy of Bayesian polynomial extrapolations from small parameter values, x, to large values of x. We consider a set of polynomials of fixed order, intended as a proxy for a fixed-order effective field theory (EFT) description of data. We employ Bayesian model averaging (BMA) to combine results from different order polynomials (EFT orders). Our study considers two 'toy problems' where the underlying function used to generate data sets is known. We use Bayesian parameter estimation to extract the polynomial coefficients that describe these data at low x. A 'naturalness' prior is imposed on the coefficients, so that they are O (1) . We BMA different polynomial degrees by weighting each according to its Bayesian evidence and compare the predictive performance of this BMA with that of the individual polynomials. The credibility intervals on the BMA forecast have the stated coverage properties more consistently than does the highest evidence polynomial, though BMA does not necessarily outperform every polynomial. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
47. Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure
- Author
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Antonio Pacifico
- Subjects
structural panel VAR ,bayesian model averaging ,compressed regression methods ,markov chains algorithms ,forecasting ,stochastic volatility ,Economics as a science ,HB71-74 - Abstract
This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspecificity involved in compressed regression models. Methodologically, a multicountry large structural Panel Vector Autoregression is compressed through a robust model averaging to select the best subset across all possible combinations of predictors, where robust stands for the use of mixtures of proper conjugate priors. Concerning dynamic analysis, volatility changes and conditional density forecasts are addressed ensuring accurate predictive performance and capability. An empirical and simulated experiment are developed to highlight and discuss the functioning of the estimating procedure and forecasting accuracy.
- Published
- 2022
- Full Text
- View/download PDF
48. Linking Pensions to Life Expectancy: Tackling Conceptual Uncertainty through Bayesian Model Averaging
- Author
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Jorge M. Bravo and Mercedes Ayuso
- Subjects
sustainability factor ,retirement age ,Bayesian model averaging ,pensions ,life expectancy ,mortality forecasting ,Mathematics ,QA1-939 - Abstract
Linking pensions to longevity developments at retirement age has been one of the most common policy responses of pension schemes to aging populations. The introduction of automatic stabilizers is primarily motivated by cost containment objectives, but there are other dimensions of welfare restructuring in the politics of pension reforms, including recalibration, rationalization, and blame avoidance for unpopular policies that involve retrenchments. This paper examines the policy designs and implications of linking entry pensions to life expectancy developments through sustainability factors or life expectancy coefficients in Finland, Portugal, and Spain. To address conceptual and specification uncertainty in policymaking, we propose and apply a Bayesian model averaging approach to stochastic mortality modeling and life expectancy computation. The results show that: (i) sustainability factors will generate substantial pension entitlement reductions in the three countries analyzed; (ii) the magnitude of the pension losses depends on the factor design; (iii) to offset pension cuts and safeguard pension adequacy, individuals will have to prolong their working lives significantly; (iv) factor designs considering cohort longevity markers would have generated higher pension cuts in countries with increasing life expectancy gap.
- Published
- 2021
- Full Text
- View/download PDF
49. Previsão do Consumo Agregado: o papel de índices de confiança do consumidor
- Author
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Patrícia Felini, Fábio Augusto Reis Gomes, and Gian Paulo Soave
- Subjects
Consumo agregado ,Consumer confidence indices ,Ponderação bayesiana de modelos ,Bayesian model averaging ,Forecast ,Aggregate consumption ,General Economics, Econometrics and Finance ,Previsão ,Índices de confiança do consumidor - Abstract
Resumo Este artigo investiga se os índices de confiança do consumidor podem melhorar as projeções do consumo agregado no Brasil, levando em conta informações dos fundamentos econômicos contidas em defasagens de indicadores financeiros e das taxas de crescimento do PIB e do volume de crédito às famílias. Nesse contexto, permitimos estruturas de defasagens distintas entre os potenciais preditores do consumo, o que dá origem a um grande espaço de potenciais modelos. Usamos, então, técnicas de ponderação bayesiana de modelos como uma estratégia agnóstica para lidar com a inerente incerteza sobre o modelo. Esta abordagem nos permitiu investigar quais regressores podem ser considerados robustos. Os resultados sugerem que PIB, crédito às famílias, retorno do mercado acionário e indicadores de confiança dos consumidores apresentam um potencial preditivo robusto na análise dentro da amostra. Finalmente, os resultados fora da amostra sugerem um papel não desprezível para os índices de confiança do consumidor na previsão da taxa de crescimento do consumo agregado no Brasil, especialmente em horizontes de previsão curtos. Abstract This paper investigates whether consumer confidence indices can improve the forecasts of aggregate consumption in Brazil, taking into account information on economic fundamentals contained in lagged financial indicators and growth rates of GDP and credit to households. In this context, we allow different lag structures for the potential predictors of consumption, which gives rise to a large space of potential models. Thus, we apply Bayesian model averaging techniques as an agnostic strategy to deal with the inherent uncertainty about the model. This approach allowed us to investigate which predictors can be considered robust. The in-sample results suggest that GDP, credit to households, return on stocks and consumer confidence indices have a robust predictive potential. Finally, the out-of-sample results suggest a not insignificant role for consumer confidence indices in forecasting the growth rate of the aggregate consumption in Brazil, especially for short-term forecasting horizons.
- Published
- 2022
50. Bayesian model averaging: improved variable selection for matched case-control studies
- Author
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Isaac See, Yi Mu, and Jonathan R. Edwards
- Subjects
model selection ,lcsh:R5-920 ,0303 health sciences ,Correlation coefficient ,030306 microbiology ,Computer science ,lcsh:Public aspects of medicine ,Model selection ,Bayesian model averaging ,Inference ,lcsh:RA1-1270 ,Feature selection ,Zellner’s g-prior ,Risk factor (finance) ,Bayesian inference ,Measure (mathematics) ,Article ,Field (computer science) ,03 medical and health sciences ,0302 clinical medicine ,Statistics ,matched case control ,030212 general & internal medicine ,lcsh:Medicine (General) ,Gibbs variable selection - Abstract
Background : The problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field. However, this approach has been criticized for not taking into account the uncertainty of the model selection process itself. This limitation can be addressed by a Bayesian model averaging approach: instead of focusing on a single model and a few factors, Bayesian model averaging considers all the models with non-negligible probabilities to make inference. Methods : This paper reports on a simulation study designed to emulate a matched case-control study and compares classical versus Bayesian model averaging selection methods. We used Matthews’s correlation coefficient to measure the quality of binary classifications. Both classical and Bayesian model averaging were also applied and compared for the analysis of a matched case-control study of patients with methicillin-resistant Staphylococcus aureus infections after hospital discharge 2011-2013. Results : Bayesian model averaging outperformed the classical approach with much lower false positive rates and higher Matthew’s correlation scores. Bayesian model averaging also produced more reliable and robust effect estimates. Conclusion : Bayesian model averaging is a conceptually simple, unified approach that produces robust results. It can be used to replace controversial P-values for case-control study in medical research. Key words: Bayesian model averaging; Gibbs variable selection; matched case control; model selection; Zellner’s g-prior
- Published
- 2022
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