Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach

Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach
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Title Cited by Year Model selection and multimodel inference: Behavioral Ecology and Sociobiology 65 1 , , Cooperative Fish and Wildlife Research Unit , Articles 1—20 Show more. SearchWorks Catalog Stanford Libraries. Model selection and multimodel inference: Physical description xxvi, p.

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Contributor Anderson, David R. Bibliography Includes bibliographical references p. Nielsen Book Data Publisher's Summary The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set Multi-Model Inference. A philosophy is presented for model-based data analysis and a general strategy outlined for the analysis of empirical data.

The book invites increased attention on a priori science hypotheses and modeling. Kullback-Leibler Information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection. The maximized log-likelihood function can be bias-corrected as an estimator of expected, relative Kullback-Leibler information.

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We wrote this book to introduce graduate students and research workers in various scienti?c disciplines to the use of information-theoretic approaches in the . Library of Congress Cataloging-in-Publication Data. Burnham, Kenneth P. Model selection and multimodel inference: a practical information-theoretic approach.