Model Selection and Inference

A Practical Information-Theoretic Approach

  • Kenneth P. Burnham
  • David R. Anderson

Table of contents

  1. Front Matter
    Pages i-xx
  2. Kenneth P. Burnham, David R. Anderson
    Pages 1-31
  3. Kenneth P. Burnham, David R. Anderson
    Pages 75-117
  4. Kenneth P. Burnham, David R. Anderson
    Pages 118-158
  5. Kenneth P. Burnham, David R. Anderson
    Pages 159-229
  6. Kenneth P. Burnham, David R. Anderson
    Pages 230-314
  7. Kenneth P. Burnham, David R. Anderson
    Pages 315-328
  8. Back Matter
    Pages 329-355

About this book

Introduction

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.

Keywords

Inference Likelihood Model Selection data analysis information theory optimization statistics

Authors and affiliations

  • Kenneth P. Burnham
    • 1
  • David R. Anderson
    • 1
  1. 1.Colorado Cooperative Fish and Wildlife Research UnitColorado State UniversityFort CollinsUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-2917-7
  • Copyright Information Springer-Verlag New York 1998
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4757-2919-1
  • Online ISBN 978-1-4757-2917-7
  • About this book