Model Selection and Model Averaging
Title | Model Selection and Model Averaging PDF eBook |
Author | Gerda Claeskens |
Publisher | |
Total Pages | 312 |
Release | 2008-07-28 |
Genre | Mathematics |
ISBN | 9780521852258 |
First book to synthesize the research and practice from the active field of model selection.
Statistical Foundations, Reasoning and Inference
Title | Statistical Foundations, Reasoning and Inference PDF eBook |
Author | Göran Kauermann |
Publisher | Springer Nature |
Total Pages | 361 |
Release | 2021-09-30 |
Genre | Mathematics |
ISBN | 3030698270 |
This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master’s students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.
Model Selection and Model Averaging
Title | Model Selection and Model Averaging PDF eBook |
Author | Gerda Claeskens |
Publisher | Cambridge University Press |
Total Pages | 312 |
Release | 2008-07-28 |
Genre | Mathematics |
ISBN | 1139471805 |
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.
Bayesian Model Selection and Statistical Modeling
Title | Bayesian Model Selection and Statistical Modeling PDF eBook |
Author | Tomohiro Ando |
Publisher | CRC Press |
Total Pages | 300 |
Release | 2010-05-27 |
Genre | Mathematics |
ISBN | 9781439836156 |
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 Multimodel Inference
Title | Model Selection and Multimodel Inference PDF eBook |
Author | Kenneth P. Burnham |
Publisher | Springer Science & Business Media |
Total Pages | 512 |
Release | 2007-05-28 |
Genre | Mathematics |
ISBN | 0387224564 |
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.
Models in Environmental Regulatory Decision Making
Title | Models in Environmental Regulatory Decision Making PDF eBook |
Author | National Research Council |
Publisher | National Academies Press |
Total Pages | 286 |
Release | 2007-08-25 |
Genre | Political Science |
ISBN | 0309110009 |
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.
Model Selection and Inference
Title | Model Selection and Inference PDF eBook |
Author | Kenneth P. Burnham |
Publisher | Springer Science & Business Media |
Total Pages | 373 |
Release | 2013-11-11 |
Genre | Mathematics |
ISBN | 1475729170 |
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.