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The effect of ionizing radiation on cells is a complex process dependent on several parameters. Cancer treatment commonly involves the use of radiotherapy. In addition to the effective killing of cancer cells, another key aspect of radiotherapy is the protection of healthy cells. An interesting position is occupied by heavy ion radiation in the field of radiotherapy due to its high relative biological effectiveness, making it an effective method of treatment. The high biological efficiency of heavy ion radiation can also pose a danger to healthy cells. The extent of cell death induced by heavy ion radiation in cells was investigated using statistical learning methods in this study. The objective was to predict the healthy cell survival rate based on the physical parameters of the available ionizing radiation. This paper is based on secondary research utilizing the PIDE database. Throughout this study, a local regression and a random forest model were generated. Their predictions were compared to the results of a linear-quadratic model commonly utilized in the field of ionizing radiation using various metrics. The relationship between dose and cell survival rate was examined using the linear-quadratic (LQM) model and local regression (LocReg). An R2 value of 88.43% was achieved for LQM and 89.86% for LocReg. Upon incorporating linear energy transfer, the random forest model attained an R2 value of 96.85%. In terms of RMSE, the linear-quadratic model yielded 9.5910−2, the local regression 9.2110−2, and the random forest 1.96 × 10−2 (lower values indicate better performance). All of these methods were also applied to a log-transformed dataset to decrease the right skewedness of the distribution of the datapoints. This significantly reduced the estimates made with LQM and LocReg (28% decrease in the case of R2), while the random forest retained nearly the same level of estimation as the untransformed data. In conclusion, it can be inferred that dose alone provides a somewhat satisfactory explanatory power for cell survival rate, but the inclusion of linear energy transfer can significantly enhance prediction accuracy in terms of variance and explanatory power. [ABSTRACT FROM AUTHOR]

Some nonlinear systems can be represented through linear parameter varying models. In this work, we address the estimation of continuous-time linear parameter varying models in output error form, using a refined instrumental variable method. A distinguished feature of a linear parameter varying model is that it has parameters that depend on an external signal called the scheduling variable. In this paper, we assume that the scheduling variable is noisy, a condition which is often met in practice, but not frequently considered in the literature. On the other hand, there are applications in which the noise-free version of the scheduling variable is smooth. Under such scenario we can simply filter the scheduling variable before estimating the linear parameter model. Nonetheless, there are cases where special smoothing techniques are required. In this study, we consider one of these special cases, and we use the well-known local regression method as smoothing technique. A numerical example based on a Monte Carlo simulation shows the benefits of the proposed approach.

Principal Component Analysis can be used to reduce the cost of Computational Fluid Dynamics simulations of turbulent reacting flows by reducing the dimensionality of the transported variables through projection of the thermochemical state onto a lower-dimensional manifold. However, because of the nonlinearity of the principal component source terms, nonlinear regression techniques must be utilized for the source terms in terms of the principal components. Unfortunately, widely available and utilized nonlinear regression techniques can have prohibitive computational requirements and/or accuracy that is highly dependent on user experience in ad hoc tuning of model architecture and hyperparameters. In this work, a new nonlinear regression approach is proposed that is both computationally efficient and automated so does not require any user input. The approach is evaluated through a priori prediction of principal component source terms using data from a Direct Numerical Simulation of a turbulent nonpremixed n -heptane/air jet flame. In particular, the proposed framework consists of local regressions whose complexity is adapted according to the local nonlinearity of the data: local linear regression when accurate enough and local Artificial Neural Networks when nonlinear regression is required. The number of local clusters for local regression is determined automatically using the Davies-Bouldin index. In addition, Bayesian optimization is utilized for model training (i.e., to select the best architectures and hyperparameters of the nonlinear regressions in an unsupervised fashion), eliminating ad hoc hand-tuning and/or expensive grid searches. Overall, compared to a single, global neural network, the new local adaptive regression approach is shown to have comparable accuracy but 69% less training time due to the utilization of local linear regression and faster training of local neural networks. [ABSTRACT FROM AUTHOR]

Vessel Monitoring Systems (VMS) and other vessel tracking data have been used for many years to map the distribution of fishing activities. Mapping areas with low levels of fishing activity can be of particular interest; for example to avoid conflicts between fishing and other ocean uses like offshore renewable energy or to protect relatively pristine ecosystems from increasing fishing pressure. A particular problem when trying to delineate areas that are lightly fished, is the relative sparsity of vessel monitoring data in these areas. This paper explores three novel methods for estimating the distribution of fishing activity from VMS data, with particular focus on lightly impacted areas. The first new method divides the area of interest into a nested grid with varying cell sizes (depending on the density of data at each location); the second new method uses Voronoi diagrams to define polygons around observations and the third method applies a local regression to generate a smooth map of fishing intensity. The new methods are compared with two established methods: applying spatial grids and interpolating fishing tracks. The track interpolation method generally performs better than any of the new methods, however it is not always possible or appropriate to apply track interpolation; in those cases the local regression method is the best alternative.

The Maximum Product of Spacing (MPS) is an alternative parameter estimation method for the Maximum Likelihood Estimator (MLE), since the MLE may not exist in some circumstances. However, the MPS estimator still has some weaknesses despite being an alternative method. This is due to the variations in distance between data points, particularly with extreme data points, as the MPS is based on the calculation of spacings in a data set. It is also possible that any slight difference in the estimation of the parameters may have a substantial impact on the fitted values of the extreme value distribution. As a consequence, it is very important to estimate the parameters of the extreme value distribution as accurately as possible. Therefore, the power of the mean function could be introduced and considered as a weight function, leading to the weighted MPS. This paper shows the improvement of the MPS method. The Weighted Maximum Product of Spacing (WMPS) is a method that gives weights to the maximization of the logarithm of the spacings in the data set. This is done to reduce the root mean square error of the models and improve the goodness of fit of the extreme value distributions. [ABSTRACT FROM AUTHOR]

Accurate estimation of wind power curves using field data is instrumental to several wind farm operations including productivity assessment, power output estimation, operations and maintenance, among others. Existing methods for estimating wind power curves mainly rely on environmental variables (e.g., wind speed, direction, density) as inputs to construct the wind-to-power relationship. This paper attempts to integrate yaw misalignment as an additional input to power curve models, constructing what is referred to hereinafter as "yaw-adjusted wind power curves." Our analysis shows that integrating yaw misalignment into power curves is non-trivial, largely due to the overwhelming impact of environmental variables (mainly wind speed) on a turbine's power output, which obscures the secondary effect of yaw errors on power production. In response, we propose a local-regression-based method which reconstructs the yaw-to-power relationship conditional on an effective neighborhood of environmental variables. Tested on operational data from two onshore wind turbines in France, our proposed approach achieves significant improvements, in terms of power estimation accuracy, relative to a set of prevalent statistical- and machine-learning-based power curve models. • A local regression approach is proposed for yaw-adjusted wind power curve modeling. • Yaw-to-power relation is locally learnt conditional on a set of environmental factors. • Tested on actual data, large gains in power curve estimation accuracy are achieved. [ABSTRACT FROM AUTHOR]

We develop a Bayesian nonparametric autoregressive model applied to flexibly estimate general transition densities exhibiting nonlinear lag dependence. Our approach is related to Bayesian density regression using Dirichlet process mixtures, with the Markovian likelihood defined through the conditional distribution obtained from the mixture. This results in a Bayesian nonparametric extension of a mixtures-of-experts model formulation. We address computational challenges to posterior sampling that arise from the Markovian structure in the likelihood. The base model is illustrated with synthetic data from a classical model for population dynamics, as well as a series of waiting times between eruptions of Old Faithful Geyser. We study inferences available through the base model before extending the methodology to include automatic relevance detection among a pre-specified set of lags. Inference for global and local lag selection is explored with additional simulation studies, and the methods are illustrated through analysis of an annual time series of pink salmon abundance in a stream in Alaska. We further explore and compare transition density estimation performance for alternative configurations of the proposed model. Supplementary materials are available online. [ABSTRACT FROM AUTHOR]

Motivated by a preclinical study in a mouse model of breast cancer, we suggest a joint modeling framework for outcomes of mixed type and measurement structures (longitudinal versus single time/time-invariant). We present an approach based on the time-varying copula models, which is used to jointly model longitudinal outcomes of mixed types via a time-varying copula, and extend the scope of these models to handle outcomes with mixed measurement structures. Our framework allows the parameters corresponding to the longitudinal outcome to be time varying and thereby enabling researchers to investigate how the response-predictor relationships change with time. We investigate the finite sample performance of this new approach via a Monte Carlo simulation study and illustrate its usefulness by an empirical analysis of the motivating preclinical study, comparing the effect of various treatments on tumor volume (longitudinal continuous response) and the number of days until tumor volume triples (time-invariant count response). Through the real-life application and the simulation study, we demonstrate that, compared with marginal modeling, the joint modeling framework offers more precision in the estimation of model parameters.

We propose a copula-based joint modeling framework for mixed longitudinal responses. Our approach permits all model parameters to vary with time, and thus will enable researchers to reveal dynamic response-predictor relationships and response-response associations. We call the new class of models TIMECOP because we model dependence using a time-varying copula. We develop a one-step estimation procedure for the TIMECOP parameter vector, and also describe how to estimate standard errors. We investigate the finite sample performance of our procedure via three simulation studies, one of which shows that our procedure performs well under ignorable missingness. We also illustrate the applicability of our approach by analyzing binary and continuous responses from the Women's Interagency HIV Study and a smoking cessation program.

This paper aimed to analyse the spatio-temporal patterns of the diffusion of SARS-CoV-2, the virus causing coronavirus 2019 (COVID-19, in the city of Bologna, the capital and largest city of the Emilia-Romagna Region in northern Italy. The study took place from February 1st, 2020 to November 20th, 2021 and accounted for space, sociodemographic characteristics and health conditions of the resident population. A second goal was to derive a model for the level of risk of being infected by SARS-CoV-2 and to identify and measure the place-specific factors associated with the disease and its determinants. Spatial heterogeneity was tested by comparing global Poisson regression (GPR) and local geographically weighted Poisson regression (GWPR) models. The key findings were that different city areas were impacted differently during the first three epidemic waves. The area-to-area influence was estimated to exert its effect over an area with 4.7 km radius. Spatio-temporal heterogeneity patterns were found to be independent of the sociodemographic and the clinical characteristics of the resident population. Significant single-individual risk factors for detected SARS-CoV-2 infection cases were old age, hypertension, diabetes and co-morbidities. More specifically, in the global model, the average SARS-CoV-2 infection rate decreased 0.93-fold in the 21–65 years age group compared to the >65 years age group, whereas hypertension, diabetes, and any other co-morbidities (present vs absent), increased 1.28-, 1.39- and 1.15-fold, respectively. The local GWPR model had a better fit better than GPR. Due to the global geographical distribution of the pandemic, local estimates are essential for mitigating or strengthening security measures.

Analyzing and processing mine wind speed monitoring data is the key to realizing intelligent ventilation and real-time calculation of the ventilation network. According to the characteristics of the artificial regulation of a mine ventilation system, a local regression fuzzy C clustering algorithm is proposed in this paper, which combines local outlier processing with global air volume state analysis. Firstly, the algorithm uses the robust local weighted regression principle to analyze and preprocess the data locally, determines the risk degree of the abnormal data according to the identified times of outliers, determines the clustering number according to the clustering validity function, and analyzes the global air volume fluctuation according to the clustering results. The results show that most outliers are identified in data preprocessing. Still, the processing of dense outliers is weak, related to the window width setting and weighting multiple. The number of clusters can represent the fluctuation of the ventilation state and the pre-processed cluster centers are 4.4% lower than the original data because most of the outliers are higher than the average data. According to the law of air volume balance, the clustering results can pave the way for the global deduction of mine wind speed. There is an implicit relationship between data preprocessing and the clustering process, and when intensive outliers are not eliminated, they may be identified as separate clusters. The research of this paper points out the direction of mine wind speed data analysis, which can provide a theoretical basis for intelligent mine ventilation and real-time calculation of the ventilation network. [ABSTRACT FROM AUTHOR]

Many Bayesian inference problems involve target distributions whose density functions are computationally expensive to evaluate. Replacing the target density with a local approximation based on a small number of carefully chosen density evaluations can significantly reduce the computational expense of Markov chain Monte Carlo (MCMC) sampling. Moreover, continual refinement of the local approximation can guarantee asymptotically exact sampling. We devise a new strategy for balancing the decay rate of the bias due to the approximation with that of the MCMC variance. We prove that the error of the resulting local approximation MCMC (LA-MCMC) algorithm decays at roughly the expected 1 / T rate, and we demonstrate this rate numerically. We also introduce an algorithmic parameter that guarantees convergence given very weak tail bounds, significantly strengthening previous convergence results. Finally, we apply LA-MCMC to a computationally intensive Bayesian inverse problem arising in groundwater hydrology. [ABSTRACT FROM AUTHOR]

Super-resolution algorithms have many weaknesses because they have low resolution and quality. These weaknesses are due to limitations and high cost of hardware implementation. Therefore, software methods are used due to its high accuracy and low cost. Super-resolution is used in a variety of applications, including satellite imagery, medicine, CCTV cameras, etc. Super-resolution is divided into two main categories: super-resolution using an input image and super-resolution using multiple input images. Considering the great use of the subject, this paper provides a method for increasing the resolution using an input image. In the proposed method, we improved the image super-resolution based on local regression and nonlocal means. The proposed method consists of two stages: a learning phase and a reconstruction phase. After the initial implementation, we compared the results with a quick and accurate method and found the difference. Then, using the Genetic algorithm, these differences were applied to the initial image. Results showed that our proposed method performed 2.4% and 2.2% better than the RAISR and LANR-NLM methods, Respectively. [ABSTRACT FROM AUTHOR]

We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation. Through a set of experiments, we demonstrate the competitive performance of the proposed approach compared to full GPR, other localized models, and deep Gaussian processes. Crucially, these performances are obtained with considerable speedups compared to standard global GPR due to the sparsification effect of the Gram matrix induced by the localization operation. [ABSTRACT FROM AUTHOR]

Multiple kernel methods less consider the intrinsic manifold structure of multiple kernel data and estimate the consensus kernel matrix with quadratic number of variables,which makes it vulnerable to the noise and outliers within multiple candidate kernels.This paper first presents the clustering method via kernelized local regression(CKLR).It captures the local structure of kernel data and employs kernel regression on the local region to predict the clustering results.Moreover,this paper further extends it to perform clustering via the multiple kernel local regression(CMKLR).We construct the kernel level local regression sparse coefficient matrix for each candidate kernel,which well characterize the kernel level manifold structure.We then aggregate all the kernel level local regression coefficients via linear weights and generate the consensus sparse local regression coefficient,which largely reduces the number of candidate variables and becomes more robust against noises and outliers within multiple kernel data.Thus,the proposed method CMKLR avoids the above two limitations.It only contains one additional hyper parameter for turning.Extensive experimental results show that the clustering performance of the proposed method on benchmark data set is better than that of 10 state-of-the-art multiple kernel clustering methods.

• A new local regression strategy is developed that outperforms commonly used strategies. • Clarified the appropriate SSPs for characterizing the current spatial difference of SOC content. • Prove the superiority of shapelet-based time-variant environmental covariates extraction. • Provided a paradigm for SOC prediction at the regional scale. The monitoring of soil organic carbon (SOC) content is of significance for the global carbon cycle and the sustainability of soil quality under climate change. SOC prediction based on multi-source remote sensing data has been integrated well into different local regression strategies and model algorithms. However, the application of mixing local regression strategies with high generalizability and extracting more advanced information from time-variant data are rare. Here, we propose a climate model partitioning strategy, compared to common local regression strategies (soil classification and spectral clustering), with the aim of improving the accuracy of regional SOC content prediction. In this study, 1248 topsoil samples were collected in Northeast China. Environmental covariates representing soil-forming elements of meteorology, organisms, terrain and parent materials factors were explored, and then different time-variant covariate pre-processing were performed, and form Dataset I (conventional mean values of covariates) and Dataset II (shapelet features extracted from covariates) according to the data type. Next, we explored the effectiveness of global regression and local regression strategies (soil classification and five scenarios of Shared Socio-economic Pathways (SSPs)-based ant colony optimization clustering) for SOC prediction with a convolutional neural network (CNN) model. The results demonstrated that the optimal SOC content prediction model with the SSP245 local regression strategy and Dataset II as input yielded the lowest root mean square error (RMSE) of 5.83 g kg−1, the highest coefficient of determination (R2) and a ratio of performance to interquartile distance (RPIQ) of 0.73 and 1.99, respectively. Second, the order of SOC prediction accuracy among the different regression strategies was SSP245 > SSP119 > SSP370 > soil classification > SSP126 > SSP585 > global regression. Third, compared with Dataset I, the CNN model-based Dataset II had a 12 % increase in average R2 values, a 5.27 % decrease in RMSE, and a 4.27 % increase in RPIQ, which indicates that the shapelet feature extraction algorithm could better mine the information of time-variant variables in SOC content assessment. Finally, we identified that CNN could perform better in regions with low spatial heterogeneity. Our results suggest that the paradigm of "local regression + feature extraction" has great potential for SOC prediction and mapping, especially for larger scales. [ABSTRACT FROM AUTHOR]

Estimating the non-market monetary values of urban amenities has become commonplace in urban planning research, particularly following Rosen's seminal article on hedonic theory in 1974. As a revealed preference method, the hedonic approach decouples the market price of a house into price components that are attributable to housing characteristics. Despite the potential contribution of this theory in a planning context, three main limitations exist in the conventional applications: (1) variable measurement issues, (2) model misspecification, and (3) the problematic common use of global regression. These flaws problematically skew our understanding of the urban structure and spatial distribution of amenities, leading to misinformed policy interventions and poor amenity planning decisions. In this article, we propose a coherent conceptual framework that addresses measurement, specification, and scale challenges to generate consistent economic estimates of local amenities. Finally, we argue that, by paying greater attention to the spatial equity of amenity values, governments can provide greater equality of opportunities in cities. [ABSTRACT FROM AUTHOR]

Analyzing and processing mine wind speed monitoring data is the key to realizing intelligent ventilation and real-time calculation of the ventilation network. According to the characteristics of the artificial regulation of a mine ventilation system, a local regression fuzzy C clustering algorithm is proposed in this paper, which combines local outlier processing with global air volume state analysis. Firstly, the algorithm uses the robust local weighted regression principle to analyze and preprocess the data locally, determines the risk degree of the abnormal data according to the identified times of outliers, determines the clustering number according to the clustering validity function, and analyzes the global air volume fluctuation according to the clustering results. The results show that most outliers are identified in data preprocessing. Still, the processing of dense outliers is weak, related to the window width setting and weighting multiple. The number of clusters can represent the fluctuation of the ventilation state and the pre-processed cluster centers are 4.4% lower than the original data because most of the outliers are higher than the average data. According to the law of air volume balance, the clustering results can pave the way for the global deduction of mine wind speed. There is an implicit relationship between data preprocessing and the clustering process, and when intensive outliers are not eliminated, they may be identified as separate clusters. The research of this paper points out the direction of mine wind speed data analysis, which can provide a theoretical basis for intelligent mine ventilation and real-time calculation of the ventilation network.

Shape from focus is a passive optical technique that uses multiple images with different focus levels. To compute the sharpness of each pixel in the image sequence, obtained by translating the object plane at a constant step size along the optical axis, focus measure operators are used. However, the focus measure operators do not provide the accurate focus value of the center pixel due to information loss between consecutive frames. In this paper, a local regression is proposed as a new fitting method for the focus curves. This method fits the focus curves, obtained by using one of the focus measure operators, by utilizing the weighted least squares regression as non-parametric regression. The experiments are performed on synthetic and real objects to demonstrate the effectiveness of the proposed method.

We propose a microstructural modeling framework for studying optimal market-making policies in a FIFO (first in first out) limit order book (order book). In this context, the limit orders, market orders, and cancel orders arrivals in the order book are modeled as point processes with intensities that only depend on the state of the order book. These are high-dimensional models which are realistic from a micro-structure point of view and have been recently developed in the literature. In this context, we consider a market maker who stands ready to buy and sell stock on a regular and continuous basis at a publicly quoted price, and identifies the strategies that maximize their P&L penalized by their inventory. An extension of the methodology is proposed to solve market-making problems where the orders arrivals are modeled using Hawkes processes with exponential kernel. We apply the theory of Markov Decision Processes and dynamic programming method to characterize analytically the solutions to our optimal market-making problem. The second part of the paper deals with the numerical aspect of the high-dimensional trading problem. We use a control randomization method combined with quantization method to compute the optimal strategies. Several computational tests are performed on simulated data to illustrate the efficiency of the computed optimal strategy. In particular, we simulated an order book with constant/ symmetric/ asymmetrical/ state dependent intensities, and compared the computed optimal strategy with naive strategies. Some codes are available on . [ABSTRACT FROM AUTHOR]

In this paper, we formally discuss a computational scheme, which combines a local weighted regression model with fuzzy transform (or F-transform for short). The latter acts as a reduction technique on the cardinality of the learning problem, resulting in a more efficient algorithm. We tested the proposed approach first through two typical benchmark problems, that is the Hénon and the Mackey–Glass chaotic time series, then we applied it to short-term forecasting problems. Short-term forecasting is important in the energy field for the management of power systems and for energy trading. Hence, we considered two typical application examples in this field, that is wind power forecasting and load forecasting. Numerical results show the effectiveness of the proposed approach through a comparison against alternative techniques. [ABSTRACT FROM AUTHOR]

Temperature has a significant influence on development rates of insects and mites. Many parametric models were built to describe the temperature-dependent development rates. However, these models provided different shapes of the curves of development rate versus temperature. For different datasets, investigators have to spend much time on considering which the parametric model is the best for describing the temperature-dependent development rates. In the present study, we encourage investigators to use an important non-parametric model, the loess method, which belongs to local regression methods. The loesS method is used to fit some published data on the development rate of aphids to check the goodness-of-fit. We find that the loess method is very flexible for fitting the given datasets. Thus, we consider that the loess method can be used to describe the effect of temperature on the development rate of insects or mites.

Estimating the non-market monetary values of urban amenities has become commonplace in urban planning research, particularly following Rosen’s seminal article on hedonic theory in 1974. As a revealed preference method, the hedonic approach decouples the market price of a house into price components that are attributable to housing characteristics. Despite the potential contribution of this theory in a planning context, three main limitations exist in the conventional applications: (1) variable measurement issues, (2) model misspecification, and (3) the problematic common use of global regression. These flaws problematically skew our understanding of the urban structure and spatial distribution of amenities, leading to misinformed policy interventions and poor amenity planning decisions. In this article, we propose a coherent conceptual framework that addresses measurement, specification, and scale challenges to generate consistent economic estimates of local amenities. Finally, we argue that, by paying greater attention to the spatial equity of amenity values, governments can provide greater equality of opportunities in cities.

A recent paper published time series of concentrations of chemicals in drinking water collected from the bottom of Lake Mead, a major American water supply reservoir. Data were compared to water level using only linear regression. This creates an opportunity for students to analyze these data further. This article presents a structured introduction to time series decomposition that compares long-term and seasonal components of a time series of a single chemical (meprobamate) with those of two supporting datasets (reservoir volume and specific conductance). For the chemical data, this must be preceded by estimation of missing datum points. Results show that linear regression analyses of time series data obscure meaningful detail and that specific conductance is the important predictor of seasonal chemical variations. To learn this, students must execute a linear regression, estimate missing data using local regression, decompose time series, and compare time series using cross-correlation. Complete R code for these exercises appears in the supplementary information. This article uses real data and requires that students make and justify key decisions about the analysis. It can be a guided or an individual project. It is scalable to instructor needs and student interests in ways that are identified clearly in this article.

Errors-in-variables (EIV) regression is widely used in econometric models. The statistical analysis becomes challenging when the regression function is discontinuous and the distribution of measurement error is unknown. In the literature, most existing jump regression methods either assume that there is no measurement error involved or require that jumps are explicitly detected before the regression function can be estimated. In some applications, however, the ultimate goal is to estimate the regression function and to preserve the jumps in the process of estimation. In this paper, we are concerned with reconstructing jump regression curve from data that involve measurement error. We propose a direct jump-preserving method that does not explicitly detect jumps. The challenge of restoring jump structure masked by measurement error is handled by local clustering. Theoretical analysis shows that the proposed curve estimator is statistically consistent. A numerical comparison with an existing jump regression method highlights its jump-preserving property. Finally, we demonstrate our method by an application to a health tax policy study in Australia. [ABSTRACT FROM AUTHOR]

In this article, we respond to 'A comment on geographically weighted regression with parameter-specific distance metrics' by Oshan et al. (2019), published in this journal, where several concerns on the parameter-specific distance metric geographically weighted regression (PSDM GWR) technique are raised. In doing so, we review the developmental timeline of the multiscale geographically weighed regression modelling framework with related and equivalent models, including flexible bandwidth GWR, conditional GWR and PSDM GWR. In our response, we have tried to answer all the concerns raised in terms of applicability, veracity, interpretability and computational efficiency of the PSDM GWR model. [ABSTRACT FROM AUTHOR]

In an increasing number of cases involving estimation of a response surface, one is often confronted with situations where there are several factors to be evaluated, but experiments are prohibitively expensive. In such scenarios, learning algorithms can actively query the user or other resources to determine the most informative settings to be tested. In this article, we propose an active learning methodology based on the fundamental idea of adding a ridge and a Laplacian penalty to the V-optimal design to shrink the weight of less significant factors, while looking for the most informative settings to be tested. To leverage the intrinsic geometry of the factor settings in highly nonlinear spaces, we generalize the proposed methodology to local regression. We also propose a simple sequential design strategy for efficient determination of subsequent experiments based on the information from previous experiments. The proposed methodology is particularly suited for problems involving expensive experiments with a high standard deviation of the error. We apply the proposed methodology to a simulated wind tunnel testing and compare the result with an existing practice. We also evaluate the estimation accuracy of the proposed methodology using the paper helicopter case study. Finally, through extensive simulated experiments, we demonstrate the performance of the proposed methodology against classic response surface methods in the literature. [ABSTRACT FROM AUTHOR]

Multiplicative interaction models are widely used in social science to examine whether the relationship between an outcome and an independent variable changes with a moderating variable. Current empirical practice tends to overlook two important problems. First, these models assume a linear interaction effect that changes at a constant rate with the moderator. Second, estimates of the conditional effects of the independent variable can be misleading if there is a lack of common support of the moderator. Replicating 46 interaction effects from 22 recent publications in five top political science journals, we find that these core assumptions often fail in practice, suggesting that a large portion of findings across all political science subfields based on interaction models are fragile and model dependent. We propose a checklist of simple diagnostics to assess the validity of these assumptions and offer flexible estimation strategies that allow for nonlinear interaction effects and safeguard against excessive extrapolation. These statistical routines are available in both R and STATA. [ABSTRACT FROM AUTHOR]

Abstract Background Pediatric hand, foot, and mouth disease (HFMD) has generally been found to be associated with climate. However, knowledge about how this association varies spatiotemporally is very limited, especially when considering the influence of local socioeconomic conditions. This study aims to identify multi-sourced HFMD environmental factors and further quantify the spatiotemporal nonstationary effects of various climate factors on HFMD occurrence. Methods We propose an innovative method, named spatiotemporally varying coefficients (STVC) model, under the Bayesian hierarchical modeling framework, for exploring both spatial and temporal nonstationary effects in climate covariates, after controlling for socioeconomic effects. We use data of monthly county-level HFMD occurrence and data of related climate and socioeconomic variables in Sichuan, China from 2009 to 2011 for our experiments. Results Cross-validation experiments showed that the STVC model achieved the best average prediction accuracy (81.98%), compared with ordinary (68.27%), temporal (72.34%), spatial (75.99%) and spatiotemporal (77.60%) ecological models. The STVC model also outperformed these models in the Bayesian model evaluation. In this study, the STVC model was able to spatialize the risk indicator odds ratio (OR) into local ORs to represent spatial and temporal varying disease-climate relationships. We detected local temporal nonlinear seasonal trends and spatial hot spots for both disease occurrence and disease-climate associations over 36 months in Sichuan, China. Among the six representative climate variables, temperature (OR = 2.59), relative humidity (OR = 1.35), and wind speed (OR = 0.65) were not only overall related to the increase of HFMD occurrence, but also demonstrated spatiotemporal variations in their local associations with HFMD. Conclusion Our findings show that county-level HFMD interventions may need to consider varying local-scale spatial and temporal disease-climate relationships. Our proposed Bayesian STVC model can capture spatiotemporal nonstationary exposure-response relationships for detailed exposure assessments and advanced risk mapping, and offers new insights to broader environmental science and spatial statistics. Graphical abstract Unlabelled Image Highlights • The Bayesian STVC model is proposed for exploring the spatiotemporal nonstationary local exposure-response relationships. • Both climate and socioeconomic factors were found to be associated with HFMD occurrence under spatiotemporal scales. • Local temporal seasonal trends and spatial hot spots of HFMD occurrence and HFMD-climate associations were detected. • The logistic STVC model spatialized odds ratio indicator into local ORs for mapping disease-environment associations. [ABSTRACT FROM AUTHOR]

Peer-to-peer accommodation has grown significantly during the last decades, supported, in part, by digital platforms. These websites make available a wide range of information intended to help the customers’ decision. All these factors, in addition to the property location, may therefore influence rental price. This paper proposes different procedures for an efficient selection of a high number of price determinants in peer-to-peer accommodation when applying the perspective of the geographically weighted regression. As a case study, these procedures have been used to find the factors affecting the rental price of properties advertised on Airbnb in Gran Canaria (Spain). The results show that geographically weighted regression obtains better indicators of goodness of fit than the traditional ordinary least squares method, making it possible to identify those attributes influencing price and how their effect varies according to property locations. Moreover, the results also show that the selection procedures working directly on geographically weighted regression obtain better results than those that take good global solutions as their starting point.