Download or read book Models in Environmental Regulatory Decision Making written by National Research Council and published by National Academies Press. This book was released on 2007-08-25 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many regulations issued by the U.S. Environmental Protection Agency (EPA) are based on the results of computer models. Models help EPA explain environmental phenomena in settings where direct observations are limited or unavailable, and anticipate the effects of agency policies on the environment, human health and the economy. Given the critical role played by models, the EPA asked the National Research Council to assess scientific issues related to the agency's selection and use of models in its decisions. The book recommends a series of guidelines and principles for improving agency models and decision-making processes. The centerpiece of the book's recommended vision is a life-cycle approach to model evaluation which includes peer review, corroboration of results, and other activities. This will enhance the agency's ability to respond to requirements from a 2001 law on information quality and improve policy development and implementation.

Download or read book Developing a Protocol for Observational Comparative Effectiveness Research A User s Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

Download or read book The Statistical Theory of Linear Systems written by E. J. Hannan and published by SIAM. This book was released on 2012-05-31 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originally published: New York: Wiley, c1988.

Download or read book Model Selection and Model Averaging written by Gerda Claeskens and published by Cambridge University Press. This book was released on 2008-07-28 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate models, overfitting is a real danger. Is the monkey who typed Hamlet actually a good writer? Choosing a model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. The uncertainties involved with model selection are tackled, with discussions of frequentist and Bayesian methods; model averaging schemes are presented. Real-data examples are complemented by derivations providing deeper insight into the methodology, and instructive exercises build familiarity with the methods. The companion website features Data sets and R code.

Download or read book Model Selection and Multimodel Inference written by Kenneth P. Burnham and published by Springer Science & Business Media. This book was released on 2007-05-28 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

Download or read book Security and Intelligent Information Systems written by Pascal Bouvry and published by Springer Science & Business Media. This book was released on 2012-01-16 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the Joint Meeting of the 2nd Luxembourg-Polish Symposium on Security and Trust and the 19th International Conference Intelligent Information Systems, held as International Joint Confererence on Security and Intelligent Information Systems, SIIS 2011, in Warsaw, Poland, in June 2011. The 29 revised full papers presented together with 2 invited lectures were carefully reviewed and selected from 60 initial submissions during two rounds of selection and improvement. The papers are organized in the following three thematic tracks: security and trust, data mining and machine learning, and natural language processing.

Download or read book Econometric Analysis of Model Selection and Model Testing written by M. Ishaq Bhatti and published by Routledge. This book was released on 2017-03-02 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years econometricians have examined the problems of diagnostic testing, specification testing, semiparametric estimation and model selection. In addition researchers have considered whether to use model testing and model selection procedures to decide the models that best fit a particular dataset. This book explores both issues with application to various regression models, including the arbitrage pricing theory models. It is ideal as a reference for statistical sciences postgraduate students, academic researchers and policy makers in understanding the current status of model building and testing techniques.

Download or read book Model Selection written by H. Linhart and published by . This book was released on 1986-11-19 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first work to deal exclusively with objective criteria for comparing statistical models. Using a simple framework, it outlines a general strategy for selecting a model and applies this strategy to develop methods useful for solving specific selection problems. Topics covered include histograms, univariate distributions, simple and multiple regression, the analysis of variance and covariance, the analysis of proportions and contingency tables, time series analysis, and spatial analysis.

Download or read book Model Selection Criteria in the Presence of Missing Data Based on the Kullback Leibler Discrepancy written by JonDavid Sparks and published by . This book was released on 2009 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: An important challenge in statistical modeling involves determining an appropriate structural form for a model to be used in making inferences and predictions. Missing data is a very common occurrence in most research settings and can easily complicate the model selection problem. Many useful procedures have been developed to estimate parameters and standard errors in the presence of missing data;however, few methods exist for determining the actual structural form of a modelwhen the data is incomplete. In this dissertation, we propose model selection criteria based on the Kullback-Leiber discrepancy that can be used in the presence of missing data. The criteria are developed by accounting for missing data using principles related to the expectation maximization (EM) algorithm and bootstrap methods. We formulate the criteria for three specific modeling frameworks: for the normal multivariate linear regression model, a generalized linear model, and a normal longitudinal regression model. In each framework, a simulation study is presented to investigate the performance of the criteria relative to their traditional counterparts. We consider a setting where the missingness is confined to the outcome, and also a setting where the missingness may occur in the outcome and/or the covariates. The results from the simulation studies indicate that our criteria provide better protection against underfitting than their traditional analogues. We outline the implementation of our methodology for a general discrepancy measure. An application is presented where the proposed criteria are utilized in a study that evaluates the driving performance of individuals with Parkinson's disease under low contrast (fog) conditions in a driving simulator.

Download or read book Handbook of Structural Equation Modeling written by Rick H. Hoyle and published by Guilford Publications. This book was released on 2023-02-17 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This accessible volume presents both the mechanics of structural equation modeling (SEM) and specific SEM strategies and applications. The editor, along with an international group of contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results"--

Download or read book Discrepancy based Model Selection Criteria Using Cross Validation written by Simon Lee Davies and published by . This book was released on 2002 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: An important component of any linear modeling problem consists of determining an appropriate size and form of the design matrix. Improper specification may substantially impact both estimators of the model parameters and predictors of the response variable: underspecification may lead to results which are severely biased, whereas overspecification may lead to results with unnecessarily high variability. Model selection criteria provide a powerful and useful tool for choosing a suitable design matrix. Once a setting has been proposed for an experiment, data can be collected, leading to a set of competing candidate models. One may then attempt to select an appropriate model from this set using a model selection criterion. In this thesis we establish four frameworks which initialize with previously proposed model selection criteria targeting well-known traditional discrepancies, namely the Kullback-Leibler discrepancy, the Gauss discrepancy, the transformed Gauss discrepancy, and the Kullback symmetric discrepancy. These criteria are developed using the bias adjustment approach. Prior work has focused on finding approximately or exactly unbiased estimators of these discrepancies. We expand on this work to additionally show that the criteria which are exactly unbiased serve as the minimum variance unbiased estimators. In many situations, the predictive ability of a candidate model is its most important attribute. In light of our interest in this property, we also concentrate on model selection techniques based on cross validation. New cross validation model selection criteria that serve as counterparts to the standard bias adjusted forms are introduced, together with descriptions of the target discrepancies upon which they are based. We then develop model selection criteria which are minimum variance unbiased estimators of the cross validation discrepancies. Furthermore, we argue that these criteria serve as approximate minimum variance unbiased estimators of the corresponding traditional discrepancies. We propose a general framework to unify and elucidate part of our cross validation criterion development. We show that for the cross validation analogue of a traditional discrepancy, we can always find a "natural" criterion which serves as an exactly unbiased estimator. We study how the cross validation criteria compare to the standard bias adjusted criteria as selection rules in the linear regression framework. This is done by concluding our development of each of the four frameworks with simulation results which illustrate how frequently each criterion identifies the correctly specified model among a sequence of nested fitted candidate models. Our results indicate that the cross validation criteria tend to outperform their bias adjusted counterparts. We close by evaluating the performance of all the model selection criteria considered throughout our work by investigating the results of a simulation study compiled using a sample of data from the Missouri Trauma Registry.

Download or read book Regression and Time Series Model Selection written by Allan D. R. McQuarrie and published by World Scientific. This book was released on 1998 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important book describes procedures for selecting a model from a large set of competing statistical models. It includes model selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are discussed, and small sample and asymptotic properties are presented. The book also provides examples and large scale simulation studies comparing the performances of information-based model selection criteria, bootstrapping, and cross-validation selection methods over a wide range of models.

Download or read book Model Selection and Multimodel Inference written by Kenneth P. Burnham and published by Springer Science & Business Media. This book was released on 2003-12-04 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

Download or read book Lake Erie International Jetport Model Feasibility Investigation written by Donald Clarence Raney and published by . This book was released on 1978 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: A nontechnical summary of the Waterways Experiment Station's (WES) efforts in a model feasibility investigation of a proposed jetport located in Lake Erie offshore Cleveland, Ohio, is presented in this report. This report is intended to present the basic concepts, procedures and results of the WES model feasibility study without technical details. The following items are some of those presented in a format suitable for the nonscientist: (a) scope and objectives of WES study; (b) factors involved in hydrodynamic modeling; (c) lake characteristics and other information required as input for the models; (d) methods for obtaining unavailable data; (e) numerical and physical model evaluation, selection and preliminary design procedures; (f) information obtained from the models; and (g) current status of WES modeling efforts. Detailed technical data and results from WES study were published by WES in a series of 12 reports. These reports are referenced throughout this report in the appropriate and related sections. (Author).

Download or read book Fundamentals of Clinical Data Science written by Pieter Kubben and published by Springer. This book was released on 2018-12-21 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Download or read book MIXED MODEL SELECTION BASED ON THE CONCEPTUAL PREDICTIVE STATISTIC written by Cheng Wenren and published by . This book was released on 2014 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model selection plays an important role in statistical literature. The objective of model selection is to choose the most appropriate model from a potential large class of candidate models that balance the increase in fit against the increment in model complexity. To facilitate the selection process, a variety of model selection criteria are employed and have been developed for optimal selection of the most appropriate model. The most popular model selection criteria are the Akaike Information Criterion (AIC, 1973. 1974) and the Bayesian Information Criterion (BIC, 1976). Over the past several decades, a number of additional model selection criteria have been proposed and investigated. One important one among these is Cp from Mallow (1973), which is based on the Gauss discrepancy. In the dissertation, we focus on the development of variants of Cp in linear mixed models. Linear mixed model theory has expanded greatly in recent years, resulting in its widespread application in many areas of research. Therefore, the improvement of Cp in linear mixed model setting will significantly increase the efficiency and effectiveness of model selection. We propose the model selection criteria following Mallow's Cp (1973) statistic in linear mixed models. The first proposed criterion is marginal Cp, called MCp. We first derive MCp based on the expected Gauss discrepancy. For the set of candidate models including the true model, we adopt a consistent estimator of correlation matrix of response data. Then we form and prove an idempotent matrix in linear mixed models, which leads to an asymptotically unbiased estimator of the expected Gauss discrepancy between a candidate model and the true model, called MCp. An improvement of MCp, called IMCp, is then proposed and proved, which is also an asymptotically unbiased estimator of the expected Gauss discrepancy. In the simulation study, a set of increasing correlation coefficients in the correlation matrix of the response variable is employed for demonstrating the performance of the proposed MCp and IMCp. The simulated data are generated in different sample sizes to investigate the effect of the sample size on the performance of the proposed criteria. The simulation results illustrate that under suitable conditions, the proposed criteria outperform AIC and BIC in selecting the correct model. The IMCp behaves best when the maximum likelihood estimator (MLE) is used. Additionally, the proposed criteria perform significantly better for highly correlated response data than for weakly correlated data. The second proposed criterion is conditional Cp, called CCp. We derive the CCp under the conditional mean of response variable. Corresponding to the case where the covariance matrix is known or unknown, we derive two versions of the conditional Cp, called TCCp and CCp, respectively, and they are proved based on the expected conditional Gauss discrepancy. When the covariance matrix is known, the TCCp is an unbiased estimator of the expected conditional Gauss discrepancy; when the covariance matrix is unknown, the CCp is an asymptotically unbiased estimator of the expected conditional Gauss discrepancy. In estimation, the best linear unbiased predictor (BLUP) is employed. The simulation results demonstrate that when the true model includes significant fixed effects variables, both TCCp and CCp perform effectively in selecting the correct model. When the variance components are unknown, the penalty term in CCp computed by the estimated effective degrees of freedom yields a very good approximation to the bias correction between the target discrepancy and the goodness-of-fit part in the proposed criteria.

Download or read book Hypothesis Testing and Model Selection in the Social Sciences written by David L. Weakliem and published by Guilford Publications. This book was released on 2016-03-09 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Examining the major approaches to hypothesis testing and model selection, this book blends statistical theory with recommendations for practice, illustrated with real-world social science examples. It systematically compares classical (frequentist) and Bayesian approaches, showing how they are applied, exploring ways to reconcile the differences between them, and evaluating key controversies and criticisms. The book also addresses the role of hypothesis testing in the evaluation of theories, the relationship between hypothesis tests and confidence intervals, and the role of prior knowledge in Bayesian estimation and Bayesian hypothesis testing. Two easily calculated alternatives to standard hypothesis tests are discussed in depth: the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The companion website ([ital]www.guilford.com/weakliem-materials[/ital]) supplies data and syntax files for the book's examples.