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Statistical model selection using the Akaike Information Criterion (AIC) and similar criteria is a useful tool for comparing multiple and non-nested models without the specification of a null model, which

Statistical model selection using the Akaike Information Criterion (AIC) and similar criteria is a useful tool for comparing multiple and non-nested models without the specification of a null model, which has made it increasingly popular in the natural and social sciences. De- spite their common usage, model selection methods are not driven by a notion of statistical confidence, so their results entail an unknown de- gree of uncertainty. This paper introduces a general framework which extends notions of Type-I and Type-II error to model selection.

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Date Created
  • 2018
Resource Type
  • Text
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    Note
    • Partial requirement for: M.S., Arizona State University, 2018
      Note type
      thesis
    • Includes bibliographical references (pages 73-76)
      Note type
      bibliography
    • Field of study: Statistics

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    by Michael J. Cullan

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