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Description: |
xix, 353 pages : illustrations ; 24 cm |
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Bibliography Note: |
Includes bibliographical references (pages 329-349) and index. |
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Contents Note: |
Contents: 1. Introduction -- 2. Information Theory and Log-Likelihood Models: A Basis for Model Selection and Inference -- 3. Practical Use of the Information-Theoretic Approach -- 4. Model-Selection Uncertainty with Examples -- 5. Monte Carlo and Example-Based Insights -- 6. Statistical Theory -- 7. Summary. |
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Summary, Etc. Note: |
Summary: 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 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. The book presents 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. This is an applied book written primarily for biologists and statisticians using models for making inferences from empirical data. People interested in the empirical sciences will find this material useful as it offers an alternative to hypothesis testing and Bayesian approaches. |
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Terms of Use: |
British Library not licensed to copy 0. Uk |
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Additional Physical Forms: |
Online version: Burnham, Kenneth P. Model selection and inference. New York : Springer, ©1998 (OCoLC)645898313 |
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Elect. Loc./Access: |
Table of contents http://www.gbv.de/dms/ilmenau/toc/241626560.PDF Kostenfrei |