Model Selection
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Model Selection
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Author : H. Linhart
language : en
Publisher:
Release Date : 1986-11-19
Model Selection written by H. Linhart and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986-11-19 with Mathematics categories.
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.
Model Selection And Model Averaging
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Author : Gerda Claeskens
language : en
Publisher: Cambridge University Press
Release Date : 2008-07-28
Model Selection And Model Averaging written by Gerda Claeskens and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-07-28 with Mathematics categories.
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.
Model Selection And Multimodel Inference
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Author : Kenneth P. Burnham
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-28
Model Selection And Multimodel Inference written by Kenneth P. Burnham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-28 with Mathematics categories.
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.
Model Selection And Multimodel Inference
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Author : Kenneth P. Burnham
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-12-04
Model Selection And Multimodel Inference written by Kenneth P. Burnham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-12-04 with Mathematics categories.
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.
Model Selection
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Author : Parhasarathi Lahiri
language : en
Publisher: IMS
Release Date : 2001
Model Selection written by Parhasarathi Lahiri and has been published by IMS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Mathematics categories.
Regression And Time Series Model Selection
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Author : Allan D R Mcquarrie
language : en
Publisher: World Scientific
Release Date : 1998-05-30
Regression And Time Series Model Selection written by Allan D R Mcquarrie and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-05-30 with Mathematics categories.
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.
Bayesian Model Selection And Statistical Modeling
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Author : Tomohiro Ando
language : en
Publisher: CRC Press
Release Date : 2010-05-27
Bayesian Model Selection And Statistical Modeling written by Tomohiro Ando and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-27 with Mathematics categories.
Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model selection procedures. It thoroughly explains the concepts, illustrates the derivations of various Bayesian model selection criteria through examples, and provides R code for implementation. The author shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors, the Bayesian predictive information criterion, and the deviance information criterion. He also provides a theoretical basis for the analysis of these criteria. In addition, the author discusses how Bayesian model averaging can simultaneously treat both model and parameter uncertainties. Selecting and constructing the appropriate statistical model significantly affect the quality of results in decision making, forecasting, stochastic structure explorations, and other problems. Helping you choose the right Bayesian model, this book focuses on the framework for Bayesian model selection and includes practical examples of model selection criteria.
Model Selection And Inference
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Author : Kenneth P. Burnham
language : en
Publisher: Springer
Release Date : 1998
Model Selection And Inference written by Kenneth P. Burnham and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Mathematics categories.
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 Model Selection
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Author : Antonio Aznar Grasa
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Econometric Model Selection written by Antonio Aznar Grasa and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Business & Economics categories.
This book proposes a new methodology for the selection of one (model) from among a set of alternative econometric models. Let us recall that a model is an abstract representation of reality which brings out what is relevant to a particular economic issue. An econometric model is also an analytical characterization of the joint probability distribution of some random variables of interest, which yields some information on how the actual economy works. This information will be useful only if it is accurate and precise; that is, the information must be far from ambiguous and close to what we observe in the real world Thus, model selection should be performed on the basis of statistics which summarize the degree of accuracy and precision of each model. A model is accurate if it predicts right; it is precise if it produces tight confidence intervals. A first general approach to model selection includes those procedures based on both characteristics, precision and accuracy. A particularly interesting example of this approach is that of Hildebrand, Laing and Rosenthal (1980). See also Hendry and Richard (1982). A second general approach includes those procedures that use only one of the two dimensions to discriminate among models. In general, most of the tests we are going to examine correspond to this category.
Model Selection And Inference
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Author : Kenneth P. Burnham
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Model Selection And Inference written by Kenneth P. Burnham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.
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.