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Statistical Theory

In: Model Selection and Inference

Author

Listed:
  • Kenneth P. Burnham

    (Colorado State University, Colorado Cooperative Fish and Wildlife Research Unit)

  • David R. Anderson

    (Colorado State University, Colorado Cooperative Fish and Wildlife Research Unit)

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.

Suggested Citation

  • Kenneth P. Burnham & David R. Anderson, 1998. "Statistical Theory," Springer Books, in: Model Selection and Inference, chapter 6, pages 230-314, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4757-2917-7_6
    DOI: 10.1007/978-1-4757-2917-7_6
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