Model Selection and Multimodel Inference
Model Selection and Multimodel Inference

Author: Kenneth P. Burnham

Publisher: Springer Science & Business Media

Published: 2007-05-28

Total Pages: 512

ISBN-13: 0387224564

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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.

Regression and Time Series Model Selection
Regression and Time Series Model Selection

Author: Allan D. R. McQuarrie

Publisher: World Scientific

Published: 1998

Total Pages: 479

ISBN-13: 9812385452

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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.

Model Selection and Error Estimation in a Nutshell
Model Selection and Error Estimation in a Nutshell

Author: Luca Oneto

Publisher: Springer

Published: 2019-07-17

Total Pages: 135

ISBN-13: 3030243591

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How can we select the best performing data-driven model? How can we rigorously estimate its generalization error? Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80’s and includes the most recent results. It discusses open problems and outlines future directions for research.

Concentration Inequalities and Model Selection
Concentration Inequalities and Model Selection

Author: Pascal Massart

Publisher: Springer

Published: 2007-04-26

Total Pages: 346

ISBN-13: 3540485031

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Concentration inequalities have been recognized as fundamental tools in several domains such as geometry of Banach spaces or random combinatorics. They also turn to be essential tools to develop a non asymptotic theory in statistics. This volume provides an overview of a non asymptotic theory for model selection. It also discusses some selected applications to variable selection, change points detection and statistical learning.

Hypothesis Testing and Model Selection in the Social Sciences
Hypothesis Testing and Model Selection in the Social Sciences

Author: David L. Weakliem

Publisher: Guilford Publications

Published: 2016-04-25

Total Pages: 217

ISBN-13: 1462525652

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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.

Feature Engineering and Selection
Feature Engineering and Selection

Author: Max Kuhn

Publisher: CRC Press

Published: 2019-07-25

Total Pages: 266

ISBN-13: 1351609467

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The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

Model Selection and Inference
Model Selection and Inference

Author: Kenneth P. Burnham

Publisher: Springer Science & Business Media

Published: 2013-11-11

Total Pages: 373

ISBN-13: 1475729170

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Statisticians and applied scientists must often select a model to fit empirical data. This book discusses the philosophy and strategy of selecting such a model using the information theory approach pioneered by Hirotugu Akaike. This approach focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. The book includes practical applications in biology and environmental science.

Econometric Analysis of Model Selection and Model Testing
Econometric Analysis of Model Selection and Model Testing

Author: M. Ishaq Bhatti

Publisher: Routledge

Published: 2017-03-02

Total Pages: 286

ISBN-13: 135194195X

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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.