Model selection and multimodel inference. A practical information-theoretic approach. 2nd ed. (English) Zbl 1005.62007

Burnham, Kenneth P.; Anderson, David R.

New York, NY: Springer. xxvi, 488 p. (2002).

From the preface: This second edition, for the review of the first edition from 1998 see Zbl 0920.62006, was prepared with three goals in mind. First, we have tried to improve the presentation of the material. Boxes now highlight essential expressions and points. Some reorganization has been done to improve the flow of concepts, and a new chapter has been added. Chapters 2 (Information and likelihood theory: A basis for model selection and inference.) and 4 (Formal inference from more than one model: Multimodel inference (MMI).) have been streamlined in view of the detailed theory provided in Chapter 7 (Statistical theory and numerical results).
Second, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but particularly in Chapters 4, 5 (Monte Carlo insights and extend examples.), and 6 (Advanced issues and deeper insights).
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 first edition. In addition, we have done substantially more thinking about the issue and reading the literature since writing the first edition, and these activities have led to further insights.

MSC:

62B10	Statistical aspects of information-theoretic topics
62-01	Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics
62-02	Research exposition (monographs, survey articles) pertaining to statistics

Citations:

Zbl 0920.62006

Software:

MuMIn; MODELTEST