Bootstrapped Information-Theoretic Model Selection with Error Control (BITSEC)

• Other Authors: Cullan, Michael (Author)
• Format: Dissertation
• Language: English
• Published: 2018
• Subjects: Statistics
• Online Access: http://hdl.handle.net/2286/R.I.51801

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