Statistical Theory

  • Kenneth P. Burnham
  • David R. Anderson

Abstract

This chapter contains theory and derivations relevant to Kullback-Leibler information-theory-based model selection. We have tried to make the other chapters of this book readable by a general audience, especially graduate students in various fields. Hence, we have reserved this chapter for the theoretical material we feel it is important to make available to statisticians and quantitative biologists. For many, it will suffice to know that this theory exists. However, we encourage persons, especially if they have some mathematical-statistical training, to read and try to understand the theory given here, because that understanding provides a much deeper knowledge of many facets of K-L-based model selection in particular, and of some general model selection issues also.

Keywords

Model Selection Mean Square Error Minimum Mean Square Error Exponential Family Fisher Information Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

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

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