Model Selection and Multimodel Inference

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

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A unique and comprehensive text on the philosophy of model-based data analysis and 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. It contains 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. The text has been written for biologists and statisticians using models for making inferences from empirical data.
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Product details

  • Hardback | 488 pages
  • 155 x 235 x 28.45mm | 1,990g
  • New York, NY, United States
  • English
  • Revised
  • 2nd ed. 2002. Corr. 3rd printing 2003
  • 51 Tables, black and white; XXVI, 488 p.
  • 0387953647
  • 9780387953649
  • 566,117

Table of contents

Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summary
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25 ratings
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