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Statistical Theory and Numerical Results

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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 believe should be made available to statisticians and quantitative biologists. For many, it will suffice to know that this theory exists. However, we encourage researchers, 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
  • Monte Carlo Sample
  • 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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  • DOI: 10.1007/978-0-387-22456-5_7
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© 2002 Springer-Verlag New York, Inc.

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(2002). Statistical Theory and Numerical Results. In: Burnham, K.P., Anderson, D.R. (eds) Model Selection and Multimodel Inference. Springer, New York, NY. https://doi.org/10.1007/978-0-387-22456-5_7

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  • DOI: https://doi.org/10.1007/978-0-387-22456-5_7

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95364-9

  • Online ISBN: 978-0-387-22456-5

  • eBook Packages: Springer Book Archive