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Model selection and multimodel inference
Name: Model selection and multimodel inference
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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. This item:Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach by Kenneth P. Burnham Hardcover $ By Kenneth P. Burnham - Model Selection and Multi-Model Inference: A Practical Information. By Kenneth P. Burnham, David R. Anderson:Model.
18 Aug AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Kenneth P. Burnham. On Jan 1, Kenneth P. Burnham (and others) published: Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 1 Introduction. 1. Objectives of the Book. 1. Background Material. 5. Inference from Data, Given a Model. 5. Likelihood and Least Squares.
The OP appears to be seeking a high-quality survey of high-quality statisticians to help assess whether one particular book is of high quality particularly with. This book is unique in that it covers the philosophy of model-based data analysis and a strategy for the analysis of empirical data. The book introduces. Citation Styles for "Model selection and multimodel inference: a practical information-theoretic approach". APA (6th ed.) Burnham, K. P., Anderson, D. R. Therefore, arguments about using AIC versus BIC for model selection cannot be from a Bayes coping with model selection uncertainty (multimodel inference). The model selection literature has been generally poor at reflecting the deep foundations of the Akaike information criterion (AIC) and at making appropriate co.