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Basic Use of the Information-Theoretic Approach

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Abstract

Model building and data analysis in the biological sciences somewhat presuppose that the investigator has some advanced education in the quantitative sciences, and statistics in particular. This requirement also implies that a researcher has substantial knowledge of statistical null hypothesis-testing approaches. Such investigators, including ourselves over the past several years, often find it difficult to understand the information-theoretic approach, only because it is conceptually so very different from the testing approach that is so familiar. Relatively speaking, the concepts and practical use of the information-theoretic approach are simpler than those of statistical hypothesis testing, and much simpler than some of the Bayesian approaches to data analysis (e.g., Laud and Ibrahim 1995 and Carlin and Chib 1995).

Keywords

  • Global Model
  • Candidate Model
  • Akaike Weight
  • Conditional Survival
  • Evidence Ratio

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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© 2002 Springer-Verlag New York, Inc.

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(2002). Basic Use of the Information-Theoretic Approach. 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_3

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  • DOI: https://doi.org/10.1007/978-0-387-22456-5_3

  • 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