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

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Springer Science & Business Media, May 28, 2007 - Mathematics - 488 pages
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.
 

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Contents

A Basis for Model
49
3
55
27
91
Basic Use of the InformationTheoretic Approach
98
Monte Carlo Insights and Extended Examples
206
Examples and Ideas Illustrated with Linear Regression
224
Estimation of Density from Line Transect Sampling
255
Summary
264
50
380
60
386
65
392
72
398
76
407
81
413
Summary
437
References
455

MonteCarloSimulations
281
ContrastingAICandBIC
347
Statistical Theory and Numerical Results
352
40
373

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