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Today, geomechanics plays a crucial role in the oil industry, particularly in enhancing production and ensuring well stability. To achieve optimal results, accurate estimation of geomechanical parameters is essential. One of the low-cost and accurate methods for estimating geomechanical parameters is the use of intelligent methods. In this research, geomechanical parameters are estimated using conventional data logs using intelligent methods. The aim of this study is to introduce a new machine learning algorithm to estimate geomechanical parameters using conventional data logs in one of the hydrocarbon field wells in southwest Iran. In this article, the shear wave velocity and uniaxial compressive strength (UCS) were estimated using machine learning algorithms. Subsequently, other geomechanical parameters were calculated based on these estimated parameters derived from machine learning algorithms. For shear wave velocity (Vs) prediction using MLP and CLM (CNN+LSTM+MLP) algorithms, First, effective features were selected using the Auto-encoder deep learning algorithm. The selected features for Vs input into the algorithms were Vp, RHOB, CALIPER, and NPHI, and then the Vs is estimated with MLP and CLM algorithm. To evaluate the results, the model was assessed using metrics such as MAE, MAPE, MSE, RMSE, NRMSE, and R2 on the train, test, and blind datasets. The CLM algorithm consistently demonstrated superior performance across all datasets, including training, testing, and blind data sets. The R2 values for blind data were $R_{MLP}^2 = 0.8727,$ R M L P 2 = 0.8727 , $R_{CLM}^2 = 0.9274$ R C L M 2 = 0.9274 , respectively. These outputs are crucial for estimating subsequent studies. Next, Elastic Young's moduli and Poisson's ratio were calculated, and the dynamic brittleness index was computed using dynamic Young's modulus and Poisson's ratio. Subsequently, UCS values were predicted using machine learning algorithms. Since there were 12 laboratory core samples of UCS available, UCS was initially calculated using relevant empirical relations and data from some available well logs. This process aimed to extrapolate these core results to cover the entire target depth range from 3551.072 to 3799.789 m. Subsequently, a relationship between UCS derived from well logs and laboratory results was established for the depths corresponding to where the laboratory samples were recorded. Following that, an autoencoder deep network was utilized to select effective features for predicting UCS. The selected features for UCS input into the algorithms were Vp, RHOB, and CALIPER. Subsequently, UCS was estimated using MLP and CLM algorithms. To evaluate the results, the model performance was assessed using metrics such as MAE, MAPE, MSE, RMSE, NRMSE, and R2 on the train, test, and blind datasets. Furthermore, the results were checked and evaluated using a set of statistical measures calculated for the train, test, and blind datasets, that $ R_{MLP}^2 = 0.9305, R_{CLM}^2 = 0.9953 $ R M L P 2 = 0.9305 , R C L M 2 = 0.9953 were obtained for blind UCS data. The results demonstrate that CLM achieves high accuracy in estimating these parameters, with deep learning algorithms showing higher determination coefficients and lower errors compared to MLP. Furthermore, UCS and tensile strength were calculated, followed by the computation of static brittleness index. The relationship between dynamic and static brittleness index was also investigated. Overall, the findings indicate that machine learning algorithms are robust and accurate methods for estimating Vs, UCS, and other geomechanical parameters using conventional logs. [ABSTRACT FROM AUTHOR]

Theories and models are central to Human Factors/Ergonomics (HFE) sciences for producing new knowledge, pushing the boundaries of the field, and providing a basis for designing systems that can improve human performance. Despite the key role, there has been less attention to what constitutes a good theory/model and how to examine the relative worth of different theories/models. This study aims to bridge this gap by (1) proposing a set of criteria for evaluating models in HFE, (2) employing a methodological approach to utilize the proposed criteria, and (3) evaluating the existing models of trust in automation (TiA) according to the proposed criteria. The resulting work provides a reference guide for researchers to examine the existing models' performance and to make meaningful comparisons between TiA models. The results also shed light on the differences among TiA models in satisfying the criteria. While conceptual models offer valuable insights into identifying the causal factors, their limitation in operationalization poses a major challenge in terms of testability and empirical validity. On the other hand, although more readily testable and possessing higher predictive power, computational models are confined to capturing only partial causal factors and have reduced explanatory power capacity. The study concludes with recommendations that in order to advance as a scientific discipline, HFE should adopt modelling approaches that can help us understand the complexities of human performance in dynamic sociotechnical systems. [ABSTRACT FROM AUTHOR]

The ultimate strength of spherical shells under external pressure has been an attractive topic in the field of marine structures. However, recent studies have revealed that the current theoretical approaches are hardly compatible with the actual hulls, which indicates that the ultimate strength models still need to be reevaluated and unified. To address this challenge, a comparative study has been conducted in this paper. Various analytical approaches and codified rules are compared through screened experiment data that have realistic imperfections and different shape parameters in the range generally applied for marine structures. The model evaluation criteria have been established by implementing statistical model uncertainty factors in terms of bias and coefficient of variation. A comparison of the results has been made from three different aspects including shell shape, R/t and geometrical parameters to evaluate their performance. Based on the calculation outcome, analysis has been made to study the theory behind those models and determine their limitations and recommended application range. [ABSTRACT FROM AUTHOR]

Fabric is produced by the weaving process through the interlacement of warp and weft yarn or knitting process through the loop formation of yarn. During these processes, there is a possibility of fabric defect formation which hinders the acceptability by the fabric consumers. Ethiopian textile factories practiced a human inspection system, a traditional means of detecting fabric quality, for monitoring textile fabric defects. Manual fabric defect detection helps to instantly correct small defects, but it is time-consuming and results in human error due to fatigue and lack of concentration. Moreover, the accuracy of recognizing the defect highly depends on the mental status of the person that checks the defects. This initiated the development of a better fabric defect identification system that helps textile experts to detect fabric defects with better precision and speed. This study proposes a vision-based fabric inspection system for plain woven gray fabrics with a uniform texture. Accordingly, a comprehensive Fabric Defect Detection Database (FDDD) is constructed. The fabric significant features were calculated using a convolutional neural network (CNN) which is a state-of-the-art technology in image processing and task analysis. The experimental result of this study shows an average accuracy of about 89% in fabric defect recognition. [ABSTRACT FROM AUTHOR]

Discharge data used to calibrate and evaluate hydrological models can be highly uncertain and this uncertainty affects the conclusions that we can draw from modelling results. We investigated the role of discharge data uncertainty and its representation in hydrological model calibration to give recommendations on methods to account for data uncertainty. We tested five different representations of discharge data uncertainty in calibrating the HBV-model for three Swiss catchments, ranging from using no information to using full empirical probability distributions for each time step. We developed a new objective function to include discharge data uncertainty, as quantified by these distributions directly in calibration to hydrological time series. This new objective function provided more reliable results than using no data uncertainty or multiple realizations of discharge time series. We recommend using the new objective function in combination with empirical or triangular distributions of the discharge data uncertainty. [ABSTRACT FROM AUTHOR]

Many important decisions depend on unknown states of the world. Society is increasingly relying on statistical predictive models to make decisions in these cases. While predictive models are useful, previous research has documented that (a) individual decision makers distrust models and (b) people's predictions are often worse than those of models. These findings indicate a lack of awareness of how to evaluate predictions generally. This includes concepts like the loss function used to aggregate errors or whether error is training error or generalisation error. To address this gap, we present three studies testing how lay people visually evaluate the predictive accuracy of models. We found that (a) participant judgements of prediction errors were more similar to absolute error than squared error (Study 1), (b) we did not detect a difference in participant reactions to training error versus generalisation error (Study 2), and (c) participants rated complex models as more accurate when comparing two models, but rated simple models as more accurate when shown single models in isolation (Study 3). When communicating about models, researchers should be aware that the public's visual evaluation of models may disagree with their method of measuring errors and that many may fail to recognise overfitting. [ABSTRACT FROM AUTHOR]

Mitigation of adverse effects of global warming relies on accurate flow projections under climate change. These projections usually focus on changes in hydrological signatures, such as 100-year floods, which are estimated through statistical analyses of simulated flows under baseline and future conditions. However, models used for these simulations are traditionally calibrated to reproduce entire flow series, rather than statistics of hydrological signatures. Here, we consider this dichotomy by testing whether performance indicators (e.g. Nash-Sutcliffe coefficient) are informative about model ability to reproduce distributions and trends in the signatures. Results of streamflow simulations in 50 high-latitude catchments with the 3DNet-Catch model show that high model performances according to traditional indicators do not provide assurance that distributions or trends in hydrological signatures are well reproduced. We therefore suggest that performance in reproducing distributions and trends in hydrological signatures should be included in the process of model selection for climate change impact studies. [ABSTRACT FROM AUTHOR]

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In item response theory, uncertainty associated with estimated item parameters can lead to greater uncertainty in subsequent analyses, such as estimating trait scores for individual examinees. Most existing methods to characterize or correct for item parameter uncertainty implicitly assume that the latent trait continuum is fixed across the posterior distribution of item parameters. However, the latent trait continuum can also be understood as an artifact of the fitted model, such that the location of this continuum is determined with error. In other words, item parameter estimation error implies uncertainty about the location of the metric. This article uses Ramsay's (1996) geometry of the latent trait metric to develop a quantitative measure of metric stability, that is, the sampling variability of the latent trait continuum implied by errors in item parameter estimation. Through a series of illustrations, it is clarified how metric stability is related to other item response model evaluation outcomes (e.g., test information, model fit), and how metric stability can be useful in identifying well-determined regions of the latent trait continuum, making sample size recommendations, and selecting a model. Overall, the proposed measure of metric stability provides meaningful and highly interpretable information to aid in item response model evaluation. [ABSTRACT FROM AUTHOR]

Fake information in social media frequently causes social issues. The amount of fake information is smaller than that of real information, this leads to class imbalance. Some improved classification methods and metrics to resolve the imbalance and evaluate model performance have been proposed, respectively. However, the existing metrics for classification methods have many limitations. This paper proposes the robust metric, L-measure, that can reasonably evaluate all models with binary class imbalance with different IRs. L-measure also require less computation than the Matthews correlation coefficient. Finally, this paper demonstrates the validity of the proposed metric under different IRs with examples from UCI and Kaggle. [ABSTRACT FROM AUTHOR]

Subject-specific tensioning of ligaments is essential for the stability of the knee joint and represents a challenging aspect in the development of finite element models. We aimed to introduce and evaluate a new procedure for the quantification of ligament prestrains from biplanar X-ray and CT data. Subject-specific model evaluation was performed by comparing predicted femorotibial kinematics with the in vitro response of six cadaveric specimens. The differences obtained using personalized models were comparable to those reported in similar studies in the literature. This study is the first step toward the use of simplified, personalized knee FE models in clinical context such as ligament balancing. [ABSTRACT FROM AUTHOR]

A comparison between 4 (near) global ocean reanalysis products is presented for the waters around New Zealand. The objective is to provide information for an educated choice of ocean estate estimate. The simulations are compared to satellite and in situ observations, and vertical sections are extracted to evaluate the representation of the main regional boundary currents and their transport. Overall, the Copernicus GLORYS reanalysis exhibits the better performance, with more realistic ocean variability and smaller biases in the water column structure. However, the BlueLink Reanalysis (BRAN) provides more realistic transport estimates of the East Auckland Current, an important boundary current connecting New Zealand to the World Ocean. All simulations have important biases in both temperature and salinity, particularly in coastal regions. Moreover, they are not able to represent coastal currents and processes. Therefore, the present study results emphasise the need for a regional ocean reanalysis and a data assimilative operational forecast system. [ABSTRACT FROM AUTHOR]

Courtyards are an effective passive strategy for improving the energy performance of buildings. However, there is a lack of accurate simulation tools for their thermal performance due to their complex thermodynamics. This paper's contribution is the coupling of a CFD model with a system of differential equations at the walls, governing surface and inner wall temperatures, providing an accurate computation of courtyard thermal performance. On this basis, boundary conditions for the standard Boussinesq equations governing temperatures, wind velocity, and pressure within the courtyard are established. Modelling results are compared with monitored data in two days of different months in a courtyard in Seville (Spain). Simultaneously, simulations carried out by existing software were performed. Resulting data showed more accuracy than existing tools, with a Root Mean Square Error of 1.19 (August) and 1.59 (October), while the corresponding values with ENVI-met were 3.31-3.4, and with Ladybug Tools 2.59-4.49. [ABSTRACT FROM AUTHOR]

We propose an extension of the Generalized Autocontour tests for dynamic specification (evaluation) of in-sample (out-of-sample) conditional densities. The new tests are based on probability integral transforms computed from bootstrap conditional densities that incorporate parameter uncertainty without relying on parametric assumptions of the error distribution. Their finite sample distributions are well approximated using standard asymptotic distributions while they are easy to implement and provide information about potential sources of misspecification. We apply the new tests to the Heterogeneous Autoregressive and the Multiplicative Error models of the VIX index and find strong evidence against the parametric assumptions of the conditional densities. [ABSTRACT FROM AUTHOR]

Discrepancy measures are often employed in problems involving the selection and assessment of statistical models. A discrepancy gauges the separation between a fitted candidate model and the underlying generating model. In this work, we consider pairwise comparisons of fitted models based on a probabilistic evaluation of the ordering of the constituent discrepancies. An estimator of the probability is derived using the bootstrap. In the framework of hypothesis testing, nested models are often compared on the basis of the p-value. Specifically, the simpler null model is favored unless the p-value is sufficiently small, in which case the null model is rejected and the more general alternative model is retained. Using suitably defined discrepancy measures, we mathematically show that, in general settings, the likelihood ratio test p-value is approximated by the bootstrapped discrepancy comparison probability (BDCP). We argue that the connection between the p-value and the BDCP leads to potentially new insights regarding the utility and limitations of the p-value. The BDCP framework also facilitates discrepancy-based inferences in settings beyond the limited confines of nested model hypothesis testing. [ABSTRACT FROM AUTHOR]

We consider using the area under an empirical receiver operating characteristic curve to test the hypothesis that a predictive index combined with a range of cutoffs performs no better than pure chance in forecasting a binary outcome. This corresponds to the null hypothesis that the area in question, denoted as AUC, is 1/2. We show that if the predictive index comes from a first-stage regression model estimated over the same data set, then testing the null based on the standard asymptotic normality results leads to severe size distortion in general settings. We then analytically derive the proper asymptotic null distribution of the empirical AUC in a special case; namely, when the first-stage regressors are Bernoulli random variables. This distribution can be utilised to construct a fully in-sample test of with correct size and more power than out-of-sample tests based on sample splitting, though practical application becomes cumbersome with more than two regressors. [ABSTRACT FROM AUTHOR]

We consider using the area under an empirical receiver operating characteristic curve to test the hypothesis that a predictive index combined with a range of cutoffs performs no better than pure chance in forecasting a binary outcome. This corresponds to the null hypothesis that the area in question, denoted as AUC, is 1/2. We show that if the predictive index comes from a first-stage regression model estimated over the same data set, then testing the null based on the standard asymptotic normality results leads to severe size distortion in general settings. We then analytically derive the proper asymptotic null distribution of the empirical AUC in a special case; namely, when the first-stage regressors are Bernoulli random variables. This distribution can be utilised to construct a fully in-sample test of with correct size and more power than out-of-sample tests based on sample splitting, though practical application becomes cumbersome with more than two regressors. [ABSTRACT FROM AUTHOR]

Drawing appropriate conclusions about a scenario for which the exposure is truly unacceptable drives appropriate exposure and risk management, and protects the health and safety of those individuals. To ensure the vast majority of these decisions are accurate, these decisions must be based upon proven approaches and tools. When these decisions are based solely on professional judgment guided by subjective inputs, however, they are more than likely wrong, and biased, underestimating the true exposure. Models have been shown anecdotally to be useful in accurately predicting exposure but their use in occupational hygiene has been limited. Possible reasons are a general lack of guidance on model selection and use and scant model input data. The lack of systematic evaluation of the models is also an important factor. This research is the second phase of work building upon the robust evaluation of the Well Mixed Room (WMR) and Near Field Far Field (NF-FF) models under controlled conditions in an exposure chamber,[5] in which good concordance between measured and modeled airborne concentrations of three solvents under a range of conditions was observed. In real world environments, the opportunity to control environmental conditions is limited and measuring the model inputs directly can be challenging; in many cases, model inputs must be estimated indirectly without measurement. These circumstances contribute to increased model input uncertainty and consequent uncertainty in the output. Field studies of model performance directly inform us about how well models predict exposures given these practical limitations, and are, therefore, an important component of model evaluation. The evaluation included ten diverse contaminant-exposure scenarios at five workplaces involving six different contaminants. A database of parameter values and measured and modeled exposures was developed and will be useful for modeling similar scenarios in the future. [ABSTRACT FROM AUTHOR]

Building performance assessment applications require multiple categories of input information. These include, aside from building construction and systems and external conditions, representations of inhabitants. It has been suggested that the representation of people as passive and static entities is unlikely to yield reliable building performance assessment and building operation planning. Rather, adequate representations of building inhabitants should account for dynamics of inhabitants’ presence in buildings and their control-oriented actions (e.g. interactions with buildings indoor environmental control devices and systems). To address these requirements, many recent model development efforts have explored the potential of advanced mathematical formalisms. However, the resulting occupancy-related behavioural models have rarely gone through a rigorous evaluation process. The present contribution is indeed motivated primarily by the lack of explicit procedures and guidelines for the evaluation of proposed user-related behavioural models. Specifically, we formulate a number of conditions that are necessary for systematic and dependable quality assessment of buildings’ inhabitants. Towards this end, we discuss both general model evaluation requirements and specific circumstances pertaining to behavioural models of building inhabitants. By using specific instances of such models, we intend to identify the requirements of a rigorous quality assurance process with regard to behavioural models in building performance assessment applications. [ABSTRACT FROM AUTHOR]

Complexity in item response theory (IRT) has traditionally been quantified by simply counting the number of freely estimated parameters in the model. However, complexity is also contingent upon the functional form of the model. We examined four popular IRT models-exploratory factor analytic, bifactor, DINA, and DINO-with different functional forms but the same number of free parameters. In comparison, a simpler (unidimensional 3PL) model was specified such that it had 1 more parameter than the previous models. All models were then evaluated according to the minimum description length principle. Specifically, each model was fit to 1,000 data sets that were randomly and uniformly sampled from the complete data space and then assessed using global and item-level fit and diagnostic measures. The findings revealed that the factor analytic and bifactor models possess a strong tendency to fit any possible data. The unidimensional 3PL model displayed minimal fitting propensity, despite the fact that it included an additional free parameter. The DINA and DINO models did not demonstrate a proclivity to fit any possible data, but they did fit well to distinct data patterns. Applied researchers and psychometricians should therefore consider functional form-and not goodness-offit alone-when selecting an IRT model. [ABSTRACT FROM AUTHOR]

Exposure judgments made without personal exposure data and based instead on subjective inputs tend to underestimate exposure, with exposure judgment accuracy not significantly more accurate than random chance. Therefore, objective inputs that contribute to more accurate decision making are needed. Models have been shown anecdotally to be useful in accurately predicting exposure but their use in occupational hygiene has been limited. This may be attributable to a general lack of guidance on model selection and use and scant model input data. The lack of systematic evaluation of the models is also an important factor. This research addresses the need to systematically evaluate two widely applicable models, the Well-Mixed Room (WMR) and Near-Field–Far-Field (NF-FF) models. The evaluation, conducted under highly controlled conditions in an exposure chamber, allowed for model inputs to be accurately measured and controlled, generating over 800 pairs of high quality measured and modeled exposure estimates. By varying conditions in the chamber one at a time, model performance across a range of conditions was evaluated using two sets of criteria: the ASTM Standard 5157 and the AIHA Exposure Assessment categorical criteria. Model performance for the WMR model was excellent, with ASTM performance criteria met for 88–97% of the pairs across the three chemicals used in the study, and 96% categorical agreement observed. Model performance for the NF-FF model, impacted somewhat by the size of the chamber was nevertheless good to excellent. NF modeled estimates met modified ASTM criteria for 67–84% of the pairs while 69–91% of FF modeled estimates met these criteria. Categorical agreement was observed for 72% and 96% of NF and FF pairs, respectively. These results support the use of the WMR and NF–FF models in guiding decision making towards improving exposure judgment accuracy. [ABSTRACT FROM AUTHOR]

Accurate quantification of soil gas diffusion is essential to understand the gas transport mechanism in soils, especially for soil greenhouse gas emissions. To date, the performance of soil gas diffusivity (Dp/D0, whereDpis the soil gas diffusion coefficient andD0is the diffusion coefficient in free air) models has seldom been evaluated for no-tilled and tilled volcanic ash soils. In the present study, six commonly used models were evaluated for volcanic ash soils under two treatments by comparing the predicted and measured soil gas diffusivities at water potentials of pF 1.3–3. The Buckingham-Burdine-Campbell (BBC), soil-water-characteristic-dependent (SWC-dependent), and two-region extended Archie’s Law (2EAL) models showed better performance for both no-tilled and tilled volcanic ash soils, which is likely because porosity and pore size parameters of bimodal soils were taken into consideration in these models. Since the BBC model showed better accuracy than the SWC-dependent and 2EAL models and required fewer, more easily measurable parameters, this study recommends the BBC model for predicting soil gas diffusivity of volcanic ash soil under different tillage managements. In future studies, the BBC model should be further tested at water potentials of pF > 3, and may be improved by including the parameters of pore continuity and saturation. [ABSTRACT FROM AUTHOR]

The development of habitat suitability models requires a large amount of data which are rarely available. In this case, researchers need to get information on the ecological features of the studied species, based on the opinion of experts or on the literature, to construct a qualitative model. However, such models cannot be rigorously evaluated, as in most cases absence points are not available. In this paper, we assess the habitat suitability for a vulnerable insectivorous plant,Pinguicula crystallinaSibth. et Smith subsp.hirtiflora(Ten.) Strid (Lentibulariaceae) in the Campania region. Our aim was to develop an expert-based, presence-only model in support of possible conservation actions. Topographic and geological features of this species suggested by the literature were used in our model. Both the Boyce index and field surveys were chosen to evaluate the model's reliability. During field surveys, 31 absence sites and 1 new presence site were identified, and differences between sites with regard to water chemistry and quality were investigated, water being an element in the species habitat. Factors that affect reliability of the model, such as the lack of a large amount of information on the species and the limited spatial resolution of geographical information system data, are discussed. [ABSTRACT FROM AUTHOR]

One of the most important issues in structural equation modeling concerns testing model fit. We propose to retain the likelihood ratio test in combination with decision criteria that increase with sample size. Specifically, rooted in Neyman–Pearson hypothesis testing, we advocate balancing α- and β-error risks. This strategy has a number of desirable consequences and addresses several objections that have been raised against the likelihood ratio test in model evaluation. First, balancing error risks avoids logical problems with Fisher-type hypotheses tests when predicting the null hypothesis (i.e., model fit). Second, both types of statistical decision errors are controlled. Third, larger samples are encouraged (rather than penalized) because both error risks diminish as the sample size increases. Finally, the strategy addresses the concern that structural equation models cannot necessarily be expected to provide an exact description of real-world phenomena. [ABSTRACT FROM AUTHOR]

Case management is a $6 billion industry that employs over 34,000 people in the United States alone. Traditionally, case management has been utilized to help patients navigate the health care system and to coordinate care in the hope of lowering costs and achieving better health outcomes. However, since enrollment into these programs is typically either universal or limited to very sick patients, studies on the cost-effectiveness of case management programs find that their performance is mixed, at best. In this article we posit an opportunity to improve outcomes and lower costs by targeting certain patients for case management and early intervention. Utilizing modern data mining methods, we develop a methodology to identify these patients, who we describe as “jumpers” because their costs are currently low but will increase significantly in the near future. Given the performance of the prediction models, we also show that unless case management can prevent over 7.5% of health care cost increases, it may benefit enrolled members but will not reduce overall costs. The article then introduces a performance bounding methodology that characterizes the best obtainable prediction accuracy on a given data set. The derived upper bound demonstrates that searching for jumpers presents a far more challenging prediction problem than identifying future high-cost members, which is the traditional approach to selecting case management candidates. [ABSTRACT FROM AUTHOR]

This study evaluated the influence of parameter values and variances and model architecture on modeled exposures, and identified important data gaps that influence lack-of-knowledge-related uncertainty, using Consexpo 4.1 as an illustrative case study. Understanding the influential determinants in exposure estimates enables more informed and appropriate use of this model and the resulting exposure estimates. In exploring the influence of parameter placement in an algorithm and of the values and variances chosen to characterize the parameters within ConsExpo, “sensitive” and “important” parameters were identified: product amount, weight fraction, exposure duration, exposure time, and ventilation rate were deemed “important,” or “always sensitive.” With this awareness, exposure assessors can strategically focus on acquiring the most robust estimates for these parameters. ConsExpo relies predominantly on three algorithms to assess the default scenarios: inhalation vapors evaporation equation using the Langmuir mass transfer, the dermal instant application with diffusion through the skin, and the oral ingestion by direct uptake algorithm. These algorithms, which do not necessarily render health conservative estimates, account for 87, 89 and 59% of the inhalation, dermal and oral default scenario assessments,respectively, according them greater influence relative to the less frequently used algorithms. Default data provided in ConsExpo may be useful to initiate assessments, but are insufficient for determining exposure acceptability or setting policy, as parameters defined by highly uncertain values produce biased estimates that may not be health conservative. Furthermore, this lack-of-knowledge uncertainty makes the magnitude of this bias uncertain. Significant data gaps persist for product amount, exposure time, and exposure duration. These “important” parameters exert influence in requiring broad values and variances to account for their uncertainty. Prioritizing them for research will not only help fill a large and influential knowledge gap, but also lead to more accurate assessments and thus refine the studies informing policy decisions. [ABSTRACT FROM AUTHOR]

Stem volume equations (overbark) were developed, using established volume equation forms, and validated using a subset of the data collected forPinus kesiyain Benguet province, Philippines. A total of 481 trees fromPinus kesiyastands in Benguet were measured through non-destructive sampling. The data set was randomly split into two subsets for initial model development (80% of the data set) and validation (20% of the data set). The performance of the different models was evaluated using evaluation statistics: fit index (FI), root mean square error (RMSE), bias (Ē), absolute mean difference (AMD) and coefficient of variation (CV%). The best model forms were selected for final model development using the combined data set (100%). Overall, volume models with two independent variables (DBH and total height) performed better than models with only one (DBH). In the validation of the models, it was observed that AMD of the models converged in the DBH classes with higher sample size. Furthermore, one of the best models in this study, Model 2, also performed better when compared to the general single volume equation developed for the non-dipterocarp species in regions 1, 2, 3 and the Cordillera Administrative Region (CAR) of the Philippines. The models developed in this study may assist forest managers acquire improved stem volume predictions ofPinus kesiyain the forests of Benguet, Philippines. [ABSTRACT FROM AUTHOR]

A mechanistic exploration of how ozone formation in the Lower Fraser Valley (LFV) has changed over a 20-year (1985–2005) retrospective period was performed using numerical models, observations, and emissions data from four key episodes selected from the 20-year period. The motivation for this study was the observed differences in trends in summertime episodic ozone concentrations recorded at various monitoring stations within the valley; stations in the western part of the valley have generally shown a noticeable reduction in episodic ozone concentrations whereas stations in the eastern part of the valley have shown little or no improvement in their maximum 8-hour averaged ozone concentrations. Concurrent with these air quality changes, there has been a well-documented reduction in ozone precursor emissions along with an observed shift in the population patterns within the valley over the 20-year period. Ozone formation for four episodes, encompassing the different meteorological regimes that occur during LFV ozone events and spanning the retrospective period, were investigated using the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emission (SMOKE)-Community Multiscale Air Quality (CMAQ) modelling system. For each episode, two simulations, intended to isolate the effects of emission changes from meteorological changes, were performed: one with emissions set at the 1985 level and the other with emissions set at the 2005 level. Based on analysis of the model output, observational data, and precursor emission inventories, we find that the Port Moody station in the western LFV remains a volatile organic compound (VOC)-sensitive location; the central part of the LFV around the town of Chilliwack has generally changed from being VOC-limited to being NOx-limited; the easternmost part of the valley around the town of Hope has been and remains NOx-limited. Furthermore, based on the observational data and numerical model output, ozone production efficiency as a function of NO has increased noticeably at Chilliwack and likely in the other eastern parts of the valley. This efficiency increase has likely offset some of the benefits resulting from local NOxemission reductions. RÉSUMÉ [Traduit par la rédaction] Nous avons effectué une exploration mécaniste de la façon dont la formation de l'ozone dans la vallée du bas Fraser (VBF) a changé au cours d'une période rétrospective de 20 ans (1985–2005) en nous servant de modèles numériques, d'observations et de données sur les émissions pour quatre épisodes clés choisis dans la période de 20 ans. Ce sont les différences observées entre les tendances dans les concentrations épisodiques d'ozone enregistrées à certaines stations de surveillance dans la vallée qui ont motivé cette étude : les stations dans la partie ouest de la vallée ont généralement affiché une réduction notable des concentrations épisodiques d'ozone alors que les stations dans la partie est de la vallée n'ont montré que peu ou pas d'amélioration dans les valeurs maximales des concentrations moyennes d'ozone sur 8 heures. Concurremment avec ces changements dans la qualité de l'air, il s'est produit une réduction bien documentée dans les émissions de précurseurs de l'ozone en même temps qu'un déplacement observé dans les configurations de population dans la vallée au cours de la période de 20 ans. Nous avons étudié la formation d'ozone au cours de quatre épisodes, englobant les différents régimes météorologiques survenus lors des événements d'ozone dans la VBF et couvrant la période rétrospective, à l'aide du système de modélisation SMOKE (Sparse Matrix Operator Kernel Emissions) – CMAQ (Community Multiscale Air Quality) du WRF (Weather Research and Forecasting). Pour chaque épisode, nous avons effectué deux simulations visant à isoler l'effet des changements dans les émissions de l'effet des changements météorologiques, l'une avec les émissions réglées au niveau de 1985 et l'autre avec les émissions réglées au niveau de 2005. En nous basant sur l'analyse de la sortie du modèle, les données d'observation et les inventaires des émissions de précurseurs, nous trouvons que la stations de Port Moody dans l'ouest de la VBF demeure un endroit sensible aux composés organiques volatiles (COV); la partie centrale de la VBF, autour de la ville de Chilliwack, a généralement changé de « limitée par les COV » à « limitée par les NOx»; la partie la plus à l'est de la vallée, autour de la ville de Hope, était et est restée « limitée par les NOx». De plus, d'après les données d'observation et la sortie du modèle numérique, l'efficacité de la production d'ozone en fonction de NO a notablement augmenté à Chilliwack et vraisemblablement dans les autres parties de l'est de la vallée. Cette augmentation d'efficacité a probablement annulé certains gains provenant des réductions dans les émissions locales de NOx. [ABSTRACT FROM AUTHOR]

We conduct a retrospective study of ozone formation in the Lower Fraser Valley (LFV), using numerical models, observations, and emission inventories in order to understand relationships between reductions in local precursor emissions and episodic ozone concentrations. Because there appears to be little or no impact from precursor emissions upwind of the LFV during ozone episodes and because background concentrations of ozone and its precursors are generally from the North Pacific Ocean and quite low, summertime ozone formation in the LFV is mostly caused by local emissions. The observed change in behaviour of ozone formation must, therefore, arise from reductions in local precursor emissions. We exploit the observed changing precursor emission–ozone concentration relationship to perform a dynamical model evaluation. Complicating the analyses are an observed shift in the population patterns within the valley over the last 25 years and a small but documented change in the tropospheric background concentration of ozone. Ozone formation for four episodes, which capture the observed changes in ozone reduction and the different meteorological types that occur during LFV ozone events, are investigated using the Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multiscale Air Quality (CMAQ) modelling system. In order to provide realistic simulations of past events, the SMOKE emission inventory is adjusted to account for temporal changes in the amount of emissions and locations of emission sources. Model output is compared with continuous observations, data collected from field campaigns, and previous modelling efforts. The WRF-SMOKE-CMAQ modelling framework is able to capture the changes in both the magnitude of the ozone concentrations and its spatial behaviour over the period of study. Many of the simulations show that the highest ozone concentrations occur outside the area sampled by the fixed monitoring network and within the LFV's numerous tributary valleys. Not all modelled episodes achieved the same agreement with observations and some of these discrepancies are likely related to shortcomings in the meteorological modelling. The model consistently overpredicts ozone at a number of stations within the City of Vancouver and underpredicts daytime NOxconcentrations there. Both results are consistent with a deficiency in NOxemissions. The model shows a changing bias over time which also suggests uncertainties in the emission backcasting. RÉSUMÉ [Traduit par la rédaction] Nous menons une étude rétrospective de la formation de l'ozone dans le vallée du bas Fraser (VBF) à l'aide de modèles numériques, d'observations et d'inventaires d’émissions dans le but de comprendre les relations entre les réductions dans les émissions locales de polluants précurseurs et les concentrations épisodiques d'ozone. Parce qu'il semble n'y avoir que peu ou pas d'impact des émissions de polluants précurseurs en amont de la VBF durant les épisodes d'ozone et parce que les concentrations de fond de l'ozone et de ses précurseurs proviennent généralement du Pacifique Nord et sont très faibles, la formation d'ozone en été dans la VBF est principalement attribuable aux émissions locales. Le changement de comportement observé dans la formation d'ozone doit par conséquent résulter de réductions dans les émissions locales de précurseurs. Nous exploitons la relation changeante observée entre les émissions de précurseurs et la concentration de l'ozone pour effectuer une évaluation par modèle dynamique. Un déplacement observé dans les configurations de population à l'intérieur de la vallée au cours des 25 dernières années et un changement, petit mais documenté, dans la concentration troposphérique de fond de l'ozone viennent compliquer les analyses. Nous étudions la formation d'ozone lors de quatre épisodes qui capturent les changements observés dans la réduction d'ozone et les différents types météorologiques qui se produisent pendant les événements d'ozone dans la VBF en nous servant du système de modélisation SMOKE (Sparse Matrix Operator Kernel Emissions) – CMAQ (Community Multiscale Air Quality) du WRF (Weather Research and Forecasting). Afin de fournir des simulations réalistes des événements passés, nous avons ajusté l'inventaire d’émissions SMOKE pour tenir compte des changements au cours du temps dans la quantité d’émissions et dans la position des sources d’émissions. Nous comparons la sortie du modèle avec les observations continues, les données recueillies lors d’études sur le terrain et les efforts de modélisation précédents. Le cadre de modélisation WRF-SMOKE-CMAQ est capable de capturer les changements dans la grandeur des concentrations d'ozone ainsi que dans son comportement spatial durant la période de l’étude. Plusieurs des simulations montrent que les plus fortes concentrations d'ozone se produisent en dehors de la région échantillonnée par le réseau fixe de surveillance et à l'intérieur des nombreuses vallées affluentes de la VBF. Tous les épisodes modélisés n'ont pas exhibé le même accord avec les observations et certaines de ces divergences sont vraisemblablement dues à des lacunes dans la modélisation météorologique. Le modèle surprévoit constamment l'ozone à certaines stations dans la ville de Vancouver et sous-prévoit les concentrations de NOxle jour à cet endroit. Les deux résultats sont cohérents avec un déficit dans les émissions de NOx. Le modèle montre un biais qui change avec le temps, ce qui porte à croire à des incertitudes dans les émissions rétrospectives. [ABSTRACT FROM AUTHOR]

Context: A statistical model that accurately predicts human forced expiratory volume in one second (FEV1) response to ozone exposure has been identified and proposed as the foundation for future risk assessments for ambient ozone. We believe that the assumptions about intra-subject variability in the published model can be improved and hypothesize that more realistic assumptions will improve the fit of the model and the accuracy of risk assessments based on the model. Objective: Identify alternate assumptions about intra-subject variability and compare goodness-of-fit for models with various variability structures. Materials and methods: Models were fit to an existing data set using a statistical program for fitting nonlinear mixed models. Goodness-of-fit was assessed using Akaike's Information Criteria (AIC) and visual examination of graphical figures showing observed and predicted values. Results: The AIC indicated that a model that assumed intra-subject variability was related to the magnitude of individual response fit the data better than a model that assumes intra-subject variability is constant across individuals and exposures (the original model). This finding was consistent with the variability of observed responses for filtered air exposures and for exposures predicted to be below the threshold for response. Conclusion: An ozone exposure-response model that assumes intra-subject variability increases with individual mean FEV1 response appears to fit the data better than one that assumes constant variability. [ABSTRACT FROM AUTHOR]

Context: A human exposure-response (E-R) model previously demonstrated to accurately predict population mean FEV1 response to ozone exposure has been proposed as the foundation for future risk assessments for ambient ozone. Objective: Fit the original and related models to a larger data set with a wider range of exposure conditions and assess agreement between observed and population mean predicted values. Materials and methods: Existing individual E-R data for 23 human controlled ozone exposure studies with a wide range of concentrations, activity levels, and exposure patterns have been obtained. The data were fit to the original model and to a version of the model that contains a threshold below which no response occurs using a statistical program for fitting nonlinear mixed models. Results: Mean predicted FEV1 responses and the predicted proportions of individuals experiencing FEV1 responses greater than 10, 15, and 20% were found to be in agreement with observed responses across a wide range of exposure conditions for both models. The threshold model, however, provided a better fit to the data than the original, particularly at the lowest levels of exposure. Conclusion: The models identified in this manuscript predict population FEV1 response characteristics for 18-35-year-old individuals exposed to ozone over a wide range of conditions and represent a substantial improvement over earlier E-R models. Because of its better fit to the data, particularly at low levels of exposure, the threshold model is likely to provide more accurate estimates of risk in future risk assessments of ozone-induced FEV1 effects. [ABSTRACT FROM AUTHOR]

Batch experiments were conducted to investigate the effects of oxytetracycline on anaerobic ammonium oxidation (anammox) process. The short-and long-term effects on anammox activity were studied by measuring ammonium (NH4 +), nitrite (NO2 −), and nitrate (NO3 −) concentrations over time. Experiments were conducted at NH4 +and NO2 − concentrations of 60–90 mg N/L and 60–190 mg N/L, respectively (NH4 +:NO2 − ratio from 1:1–1:2.25), oxytetracycline concentrations of 10–100 mg/L, and biomass concentrations of 300–800 mg/L. In the short-term study, anammox activity was inhibited by all oxytetracycline concentrations studied. However, daily addition of oxytetracycline to a concentration of 5 ± 3.5 mg/L in the anammox sequencing batch reactor completely inhibited anammox activity in the fifth week. Fluorescent in situ hybridization was used to identify autotrophic ammonium oxidizing bacteria (Nitrosomonas spp., Nitrobacter spp., Nitrospira spp., Candidatus Brocadia anammoxidans, and Candidatus Kuenenia stuttgartiensis). The population of anammox culture was significantly decreased while Nitrosomonas spp. and Nitrospira spp. increased in the fifth week compared with the first week of experiment. A not-competitive model fit the anammox inhibition data at oxytetracycline concentrations of 0–100 mg/L quite well with Vmax of 0.0435 mg N/mg VSS-hr and Ki of 54.66 mg/L. [ABSTRACT FROM AUTHOR]

A key issue facing contemporary agent-based land-use models (ABLUMs) is model evaluation. In this article, we outline some of the epistemological problems facing the evaluation of ABLUMs, including the definition of boundaries for modelling open systems. In light of these issues and given the characteristics of ABLUMs, participatory model evaluation by local stakeholders may be a preferable avenue to pursue. We present a case study of participatory model evaluation for an agent-based model designed to examine the impacts of land-use/cover change on wildfire regimes for a region of Spain. Although model output was endorsed by interviewees as credible, several alterations to model structure were suggested. Of broader interest, we found that some interviewees conflated model structure with scenario boundary conditions. If an interactive participatory modelling approach is not possible, an emphasis on ensuring that stakeholders understand the distinction between model structure and scenario boundary conditions will be particularly important. [ABSTRACT FROM AUTHOR]

The shadow economy is an extensive phenomenon worldwide. It poses several questions, the consequences of fluctuations in economic activity being among the major ones. Based on official data, this paper establishes a set of cyclical properties of macroeconomic aggregates and studies how these vary across countries with the size of the unofficial sector. Through comparisons with the existing literature on business cycles in economies featuring underground activities, the obtained 'stylized facts' are used to test the relevance of theoretical predictions on the influence of the shadow economy. Using this procedure allows us to confirm that the evidence is not entirely of the sort suggested in business cycle models. In particular, some important macro aggregates and cyclical properties have been neglected in the analysis altogether, while others have been paid too much attention for no apparent empirical reason. Some possible avenues for future research can be drawn from this exercise. [ABSTRACT FROM AUTHOR]

We identify four different approaches to estimate the regional and local impacts of tourism based on national accounts and economic modelling: The supply approach, the simple demand or commodity approach, the simple satellite account approach involving tourism satellite accounts based on social accounting, and the extended satellite account approach. Based upon a general interregional quantity model for tourism, empirical evidence on the impacts of tourism on 98 Danish municipalities is presented. We conclude that the four approaches give very different results, both in absolute and in relative terms. [ABSTRACT FROM AUTHOR]

In New Zealand and worldwide, growers are increasingly being required to demonstrate that their nutrient management does not have adverse effects on the environment. Since routine direct measurements of nitrogen (N) losses at an appropriate scale are currently unfeasible, a modelling tool that can be used to assess losses, both actual and potential, from farming systems is therefore needed. We present the upgraded N balance module for cropping systems (OVCrop) developed to be incorporated into the OVERSEER® Nutrient Budgets model. It presents an easy-to-use interface, requiring few but meaningful inputs, with the minimum flexibility needed to describe management of real farming systems. Following the developmental guidelines of OVERSEER® Nutrient Budget, OVCrop is designed to provide the long-term annual average of N leaching from any user-defined cropping rotation. OVCrop has been parameterized based on process-based model simulations for typical farm systems and environments of New Zealand. The intended use of the OVCrop is to help on-farm nutrient management and to demonstrate future compliance to N leaching regulations. OVCrop is a robust tool, simple enough to be used by people with a low to medium level of expertise, such as farmers and regional councils. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to evaluate the ability of a human exposure-response model, which describes ozone-induced changes in forced expiratory volume in 1 second (FEV1) across a wide range of dynamic exposure conditions, to predict responses in independent data. We first conducted an n-fold cross-validation of the model using samples of the original EPA data from which the model was developed. We then identified seven more recently published studies with controlled exposures to a wide range of ozone exposure patterns relevant to the current ambient ozone health standard and used the model to calculate the mean predicted responses for the exposure conditions of the individual studies that we compared to the mean observed responses reported in these studies. The n-fold cross-validation indicated good internal agreement between mean predicted and mean observed responses in the original data used to develop the model. The model accurately captured the patterns of response in each of the seven independent studies with a tendency to overpredict the observed responses by about 1 percentage point of FEV1 decrement on average. We conclude that the model is currently capable of predicting human FEV1 responses across a wide range of dynamic exposure conditions and anticipate further improvements in predictions with the addition of low-concentration exposure data. [ABSTRACT FROM AUTHOR]

I comment on the controversy between McCloskey and Ziliak and Hoover and Siegler on statistical versus economic significance, in the March 2008 issue of the Journal of Economic Methodology. I argue that while McCloskey and Ziliak are right in emphasizing 'real error', i.e. non-sampling error that cannot be eliminated through specification testing, they fail to acknowledge those areas in economics, e.g. rational expectations macroeconomics and asset pricing, where researchers clearly distinguish between statistical and economic significance and where statistical testing plays a relatively minor role in model evaluation. In these areas models are treated as inherently misspecified and, consequently, are evaluated empirically by other methods than statistical tests. I also criticize McCloskey and Ziliak for their strong focus on the size of parameter estimates while neglecting the important question of how to obtain reliable estimates, and I argue that significance tests are useful tools in those cases where a statistical model serves as input in the quantification of an economic model. Finally, I provide a specific example from economics - asset return predictability - where the distinction between statistical and economic significance is well appreciated, but which also shows how statistical tests have contributed to our substantive economic understanding. [ABSTRACT FROM AUTHOR]

An efficient graph-based strategy for performing the exhaustive k-fold cross-validation procedure is proposed. All training (and testing) subsets are presented as nodes of a complete weighted graph. The arcs between the nodes indicate the different possibilities for deriving the solution of the destination node given the solution of the source node. The weights of the arcs represent the complexities of (the numerical operations involved in) updating and downdating the corresponding data matrices. The complete graph with arcs connecting every pair of nodes is defined and its properties are investigated. The optimum way of performing the exhaustive k-fold cross-validation is equivalent in deriving the path within the graph that has the minimum computational complexity. Furthermore, a generalization of the complete k-fold cross-validation graph is used to derive new strategies for performing random k-fold cross-validations. The proposed strategies generate additional nodes during the computations, which are part of the generalized graph. The additional nodes represent new models which have not been required initially, but provide additional information about the evaluated model. The advantages and the drawbacks of the proposed strategies are discussed. Numerical results are presented and analyzed. Finally the computation of all nearest neighbors of a given node is also considered. The Fortran 90 source code for the algorithms in the manuscript is available on-line. [ABSTRACT FROM AUTHOR]

Statistical models applied to genetic studies commonly assume linear relationships (between disease and risk factors) and simple distributional forms (by relying on asymptotic methods) for inference. However, when the sample size is small, inference using traditional asymptotic models can be problematic. Moreover, the gene-disease relationship is not always linear. In this article, we present a new nonparametric Bayesian method for model assessment, and we demonstrate the advantages of this approach particularly when the sample size is small and/or the true model is non-linear. We evaluate our approach on simulated data and find that it performs substantially better than alternative models. We also apply our method to two real studies: diagnosis of conventional high-grade non-metastatic osteosarcoma, and survival in Burkitt's lymphoma. [ABSTRACT FROM AUTHOR]

We describe a method for evaluating an ensemble of predictive models given a sample of observations comprising the model predictions and the outcome event measured with error. Our formulation allows us to simultaneously estimate measurement error parameters, true outcome--the "gold standard"--and a relative weighting of the predictive scores. We describe conditions necessary to estimate the gold standard and to calibrate these estimates and detail how our approach is related to, but distinct from, standard model combination techniques. We apply our approach to data from a study to evaluate a collection of BRCA1/BRCA2 gene mutation prediction scores. In this example, genotype is measured with error by one or more genetic assays. We estimate true genotype for each individual in the data set, operating characteristics of the commonly used genotyping procedures, and a relative weighting of the scores. Finally, we compare the scores against the gold standard genotype and find that Mendelian scores are, on average, the more refined and better calibrated of those considered and that the comparison is sensitive to measurement error in the gold standard. [ABSTRACT FROM AUTHOR]

Growth modelling is an important and effective tool for evaluating the effects of a particular management action on the future development of a forest ecosystem. However, such necessary growth models are not available for many indigenous tree species in India. Acacia nilotica is an important multipurpose tree species found in India and growth models are required for proper management of the species in the region. This paper presents equations for estimating potential stand density and predicting basal area in pure even-aged stands of A. nilotica in Gujarat State of India. Although no thinning was suggested, decrease in the number of trees in the stands was observed because of mortality due to overcrowding and some biotic factors. Relationships between quadratic mean diameter and stems per hectare were developed, which was used to establish the limiting density line. Eight different stand level models, belonging to the path invariant algebraic difference form of a non-linear growth function, were compared for projecting basal area. They can be used to predict future basal area as a function of stand variables like dominant height and stem number per hectare and are crucial for evaluating different silvicultural treatment options. The performance of the models was evaluated using different statistical criteria to recommend the suitable model for projecting the basal area in A. nilotica stands. [ABSTRACT FROM AUTHOR]

This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the non-linear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models and a DSGE model that was solved with a second-order perturbation method. [ABSTRACT FROM AUTHOR]

The paper that An and Schorfheide have written is an excellent piece of work and will become a useful reference for teaching and consultation purposes. The paper discusses in an articulate and convincing manner almost everything that one could think of covering in such a review. This makes the task of the commentator difficult. Nevertheless, I will attempt to add few insights on three issues which, in my opinion, play an important role in applied work and in the interpretation of the estimation result. In particular, I will discuss a) the sensitivity of posterior distributions to prior spreads; b) the effects of model misspecification and an approach to model respecification; c) parameter identification and its consequences for posterior inference. [ABSTRACT FROM AUTHOR]

Theories of interest intermediation maintain that the formation of bargaining positions in the European Union follows a distinctive bargaining style. This article evaluates such claims empirically. It compares the predictive accuracy of Nash bargaining models which take the salient features of five types of interest intermediation into account. We show that the interaction between government agencies, interest groups and parties in the formation of EU legislation is largely étatiste. When important private interests are at stake, the pattern is also quite often clientelistic or corporatist. The dominance of the state in the less politicized decision-making processes is apparent in all four countries under consideration: Finland, Germany, Great Britain, and the Netherlands. Consociationalist arrangements are rare in this arena of public policy-making. [ABSTRACT FROM AUTHOR]

This article provides new tools for the evaluation of dynamic stochastic general equilibrium (DSGE) models and applies them to a large-scale new Keynesian model. We approximate the DSGE model by a vector autoregression, and then systematically relax the implied cross-equation restrictions and document how the model fit changes. We also compare the DSGE model's impulse responses to structural shocks with those obtained after relaxing its restrictions. We find that the degree of misspecification in this large-scale DSGE model is no longer so large as to prevent its use in day-to-day policy analysis, yet is not small enough to be ignored. [ABSTRACT FROM AUTHOR]

Two types of state-switching models for U.S. real output have been proposed: models that switch randomly between states and models that switch states deterministically, as in the threshold autoregressive model of Potter. These models have been justified primarily on how well they fit the sample data, yielding statistically significant estimates of the model coefficients. Here we propose a new approach to the evaluation of an estimated nonlinear time series model that provides a complement to existing methods based on in-sample fit or on out-of-sample forecasting. In this new approach, a battery of distinct nonlinearity tests is applied to the sample data, resulting in a set of p-values for rejecting the null hypothesis of a linear generating mechanism. This set of p-values is taken to be a "stylized fact" characterizing the nonlinear serial dependence in the generating mechanism of the time series. The effectiveness of an estimated nonlinear model for this time series is then evaluated in terms of the congruence between this stylized fact and a set of nonlinearity test results obtained from data simulated using the estimated model. In particular, we derive a portmanteau statistic based on this set of nonlinearity test p-values that allows us to test the proposition that a given model adequately captures the nonlinear serial dependence in the sample data. We apply the method to several estimated state-switching models of U.S. real output. [ABSTRACT FROM AUTHOR]

Monitoring of stable water isotopes ( 18 O and 2 H) in precipitation and surface waters in the Mackenzie River basin of northern Canada has created new opportunities for researchers to study the complex hydrology and hydroclimatology of this remote region [1]. A number of prior studies have used stable isotope data to investigate aspects of the hydrological regime of the wetland-dominated terrain near Fort Simpson, Northwest Territories, Canada [2, 3]. The present paper compares estimates of groundwater contributions to streamflow derived using the WATFLOOD distributed hydrological model, equipped with a new water isotope tracer module, with the results of conventional isotope hydrograph separation [4] for five wetland-dominated catchments along the lower Liard River. The comparison reveals highly promising agreement, verifying that the hydrological model is simulating groundwater flow contributions to total streamflow with reasonable fidelity, especially during the crucial snowmelt period. Sensitivity analysis of the WATFLOOD simulations also reveals intriguing features about runoff generation from channelized fens, which may contribute less to streamflow than previously thought. [ABSTRACT FROM AUTHOR]

Numerical indices are commonly used as tools to aid wildfire management and hazard assessment. Although the use of such indices is widespread, assessment of these indices in their respective regions of application is rare. We evaluate the effectiveness of the burning index (BI) for predicting wildfire occurrences in Los Angeles County, California using space-time point-process models. These models are based on an additive decomposition of the conditional intensity, with separate terms used to describe spatial and seasonal variability as well as contributions from the BI. We fit the models to wildfire and BI data from the years 1976-2000 using a combination of nonparametric kernel-smoothing methods and parametric maximum likelihood. In addition to using the Akaike information criterion (AIC) to compare competing models, we use new multidimensional residual methods based on approximate random thinning and rescaling to detect departures from the models and to ascertain the precise contribution of the BI to predicting wildfire occurrence. We find that although the BI appears to have a positive impact on wildfire prediction, the contribution is relatively small after taking into account natural seasonal and spatial variation. In particular, the BI does not appear to take into account increased activity during the years 1979-1981 and can overpredict during the early months of the year. [ABSTRACT FROM AUTHOR]

A model of Markov-dependent trials is considered that leads to a generalization of the binomial distribution iii the context of evaluating models of a time series by exploiting the sequential nature of model-based predictions. Adopting an evaluation method similar in nature to that suggested by Xekalaki and Katti [Xekalaki. E. Katti, S. (1984). A technique for evaluating forecasting models, thorn. J. 26: 1 73-184]. the behavior of the model is assigned a score that reflects the concordance or discordance of predicted and observed values for each of a sequence of points in time. The resulting series of scores leads to a final rating which is considered as a measure of the predictive ability of the model. The Markov-dependent distribution is used to develop exact theory for the construction of conlidence intervals and for testing hypotheses pertaining to the forecasting protential of a model. Some asymptotic theory is also developed. [ABSTRACT FROM AUTHOR]