Bootstrapped Information-Theoretic Model Selection with Error Control (BITSEC)
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...
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ndltd-asu.edu-item-518012019-02-02T03:01:23Z Bootstrapped Information-Theoretic Model Selection with Error Control (BITSEC) 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 Cullan, Michael (Author) Sterner, Beckett (Advisor) Fricks, John (Committee member) Kao, Ming-Hung (Committee member) Arizona State University (Publisher) Statistics eng 83 pages Masters Thesis Statistics 2018 Masters Thesis http://hdl.handle.net/2286/R.I.51801 http://rightsstatements.org/vocab/InC/1.0/ 2018 |
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language |
English |
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Dissertation |
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Statistics |
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Statistics Bootstrapped Information-Theoretic Model Selection with Error Control (BITSEC) |
description |
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 |
author2 |
Cullan, Michael (Author) |
author_facet |
Cullan, Michael (Author) |
title |
Bootstrapped Information-Theoretic Model Selection with Error Control (BITSEC) |
title_short |
Bootstrapped Information-Theoretic Model Selection with Error Control (BITSEC) |
title_full |
Bootstrapped Information-Theoretic Model Selection with Error Control (BITSEC) |
title_fullStr |
Bootstrapped Information-Theoretic Model Selection with Error Control (BITSEC) |
title_full_unstemmed |
Bootstrapped Information-Theoretic Model Selection with Error Control (BITSEC) |
title_sort |
bootstrapped information-theoretic model selection with error control (bitsec) |
publishDate |
2018 |
url |
http://hdl.handle.net/2286/R.I.51801 |
_version_ |
1718970087383236608 |