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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- Partial requirement for: M.S., Arizona State University, 2018Note typethesis
- Includes bibliographical references (pages 73-76)Note typebibliography
- Field of study: Statistics