Model Selection and Multimodel Inference

1. Front Matter

   Pages I-XXVI

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2. Introduction

   Pages 1-48

3. Information and Likelihood Theory: A Basis for Model Selection and Inference

   Pages 49-97

4. Basic Use of the Information-Theoretic Approach

   Pages 98-148

5. Formal Inference From More Than One Model: Multimodel Inference (MMI)

   Pages 149-205

6. Monte Carlo Insights and Extended Examples

   Pages 206-266

7. Advanced Issues and Deeper Insights

   Pages 267-351

8. Statistical Theory and Numerical Results

   Pages 352-436

9. Summary

   Pages 437-454

10. Back Matter

    Pages 455-488

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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 analysis of empirical data. These methods allow the data-based selection of a “best” model and a ranking and weighting of the remaining models in a pre-de?ned set. Traditional statistical inference can then be based on this selected best model. However, we now emphasize that information-theoretic approaches allow formal inference to be based on more than one model (m- timodel inference). Such procedures lead to more robust inferences in many cases, and we advocate these approaches throughout the book. The second edition was prepared with three goals in mind. First, we have tried to improve the presentation of the material. Boxes now highlight ess- tial expressions and points. Some reorganization has been done to improve the ?ow of concepts, and a new chapter has been added. Chapters 2 and 4 have been streamlined in view of the detailed theory provided in Chapter 7. S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6. Third, new technical material has been added to Chapters 5 and 6. Well over 100 new references to the technical literature are given. These changes result primarily from our experiences while giving several seminars, workshops, and graduate courses on material in the ?rst e- tion.

• Estimator
• Inference
• Likelihood
• Model Selection
• data analysis
• information theory

• Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, Fort Collins, USA

  Kenneth P. Burnham, David R. Anderson

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