An edition of Model selection and inference (1998)

Model selection and inference

a practical information-theoretic approach

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Last edited by ImportBot
February 17, 2024 | History
An edition of Model selection and inference (1998)

Model selection and inference

a practical information-theoretic approach

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.

Publish Date
Publisher
Springer
Language
English
Pages
353

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Edition Availability
Cover of: Model selection and inference
Model selection and inference: a practical information-theoretic approach
1998, Springer
in English
Cover of: Model Selection and Inference
Model Selection and Inference: A Practical Information-Theoretic Approach
1998, Springer New York, Springer
in English

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Book Details


Edition Notes

Includes bibliographical references (p. [329]-349) and index.

Published in
New York

Classifications

Dewey Decimal Class
570/.1/51
Library of Congress
QH323.5 .B87 1998

The Physical Object

Pagination
xix, 353 p. :
Number of pages
353

ID Numbers

Open Library
OL353383M
ISBN 10
0387985042
LCCN
98013046
OCLC/WorldCat
38542622
Library Thing
9009138
Goodreads
4559024

Work Description

We wrote this book to introduce graduate students and research workers in var­ ious scientific disciplines to the use of information-theoretic approaches in the analysis of empirical data. In its fully developed form, the information-theoretic approach allows inference based on more than one model (including estimates of unconditional precision); in its initial form, it is useful in selecting a "best" model and ranking the remaining models. We believe that often the critical issue in data analysis is the selection of a good approximating model that best represents the inference supported by the data (an estimated "best approximating model"). In­ formation theory includes the well-known Kullback-Leibler "distance" between two models (actually, probability distributions), and this represents a fundamental quantity in science. In 1973, Hirotugu Akaike derived an estimator of the (relative) Kullback-Leibler distance based on Fisher's maximized log-likelihood. His mea­ sure, now called Akaike 's information criterion (AIC), provided a new paradigm for model selection in the analysis of empirical data. His approach, with a funda­ mental link to information theory, is relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case. We do not accept the notion that there is a simple, "true model" in the biological sciences.

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History

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February 17, 2024 Edited by ImportBot import existing book
December 8, 2022 Edited by MARC Bot import new book
April 28, 2010 Edited by Open Library Bot Linked existing covers to the work.
February 1, 2010 Edited by WorkBot add more information to works
December 9, 2009 Created by WorkBot add works page