abstract: 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. A theo- retical method for controlling Type-I error using Difference of Goodness of Fit (DGOF) distributions is given, along with a bootstrap approach that approximates the procedure. Results are presented for simulated experiments using normal distributions, random walk models, nested linear regression, and nonnested regression including nonlinear mod- els. Tests are performed using an R package developed by the author which will be made publicly available on journal publication of research results. / Dissertation/Thesis / Masters Thesis Statistics 2018
| Identifer | oai:union.ndltd.org:asu.edu/item:51801 |
| Date | January 2018 |
| Contributors | Cullan, Michael (Author), Sterner, Beckett (Advisor), Fricks, John (Committee member), Kao, Ming-Hung (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 83 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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