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Model Selection and Multimodel Inference

Author: Kenneth P. Burnham

Publisher: Springer Science & Business Media

Published: 2007-05-28

Total Pages: 512

ISBN-13: 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.

Model Selection and Multimodel Inference

Author: Kenneth P. Burnham

Publisher: Springer Science & Business Media

Published: 2003-12-04

Total Pages: 512

ISBN-13: 0387953647

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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.

Model Selection and Multimodel Inference

Author: Kenneth P. Burnham

Publisher:

Published: 2014-01-15

Total Pages: 520

ISBN-13: 9781475777116

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The Spencers of Amberson Avenue

Author: Ethel Spencer

Publisher: University of Pittsburgh Press

Published: 2010-09-24

Total Pages: 208

ISBN-13: 9780822971344

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This appealing memoir introduces the family of Charles Hart Spencer and his wife Mary Acheson: seven children born between 1884 and 1895. It also introduces a large Victorian house in Shadyside (a Pittsburgh neighborhood) and a middle-class way of life at the turn of the century. Mr. Spencer, who worked--not very happily--for Henry Clay Frick, was one of the growing number of middle-management employees in American industrial cities in the 1880s and 1890s. His income, which supported his family of nine, a cook, two regular nurses, and at times a wet nurse and her baby, guaranteed a comfortable life but not a luxurious one. In the words of the editors, the Spencers represent a class that "too often stands silent or stereotyped as we rush forward toward the greater glamour of the robber barons or their immigrant workers." Through the eyes of Ethel Spencer, the third daughter, we are led with warmth and humor through the routine of everyday life in this household: school, play, church on Sundays, illness, family celebrations, and vacations. Ethel was an observant child, with little sentimentality, and she wrote her memoir in later life as a professor of English with a gift for clear prose and the instincts of an anthropologist. As the editors observe, her memoir is "a fascinating insight into one kind of urban life of three generations ago."

Model Based Inference in the Life Sciences

Author: David R. Anderson

Publisher: Springer Science & Business Media

Published: 2007-12-22

Total Pages: 203

ISBN-13: 0387740759

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This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.

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.

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

Author: Franzi Korner-Nievergelt

Publisher: Academic Press

Published: 2015-04-04

Total Pages: 329

ISBN-13: 0128016787

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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest Written in a step-by-step approach that allows for eased understanding by non-statisticians Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data All example data as well as additional functions are provided in the R-package blmeco

Information Criteria and Statistical Modeling

Author: Sadanori Konishi

Publisher: Springer Science & Business Media

Published: 2008

Total Pages: 282

ISBN-13: 0387718869

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Statistical modeling is a critical tool in scientific research. This book provides comprehensive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. The authors expect this work to be of great value not just to statisticians but also to researchers and practitioners in various fields of research such as information science, computer science, engineering, bioinformatics, economics, marketing and environmental science. It’s a crucial area of study, as statistical models are used to understand phenomena with uncertainty and to determine the structure of complex systems. They’re also used to control such systems, as well as to make reliable predictions in various natural and social science fields.

Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology

Author: László Zsolt Garamszegi

Publisher: Springer

Published: 2014-07-29

Total Pages: 552

ISBN-13: 3662435500

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Phylogenetic comparative approaches are powerful analytical tools for making evolutionary inferences from interspecific data and phylogenies. The phylogenetic toolkit available to evolutionary biologists is currently growing at an incredible speed, but most methodological papers are published in the specialized statistical literature and many are incomprehensible for the user community. This textbook provides an overview of several newly developed phylogenetic comparative methods that allow to investigate a broad array of questions on how phenotypic characters evolve along the branches of phylogeny and how such mechanisms shape complex animal communities and interspecific interactions. The individual chapters were written by the leading experts in the field and using a language that is accessible for practicing evolutionary biologists. The authors carefully explain the philosophy behind different methodologies and provide pointers – mostly using a dynamically developing online interface – on how these methods can be implemented in practice. These “conceptual” and “practical” materials are essential for expanding the qualification of both students and scientists, but also offer a valuable resource for educators. Another value of the book are the accompanying online resources (available at: http://www.mpcm-evolution.com), where the authors post and permanently update practical materials to help embed methods into practice.