Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals

Model averaging is a technique used to account for model uncertainty, in both Bayesian and frequentist multimodel inferences. In this paper, we compare the performance of model-averaged Bayesian credible intervals and frequentist confidence intervals. Frequentist intervals are constructed according...

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Main Author: Daniel Turek
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2015/420483
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author Daniel Turek
author_facet Daniel Turek
author_sort Daniel Turek
collection DOAJ
description Model averaging is a technique used to account for model uncertainty, in both Bayesian and frequentist multimodel inferences. In this paper, we compare the performance of model-averaged Bayesian credible intervals and frequentist confidence intervals. Frequentist intervals are constructed according to the model-averaged tail area (MATA) methodology. Differences between the Bayesian and frequentist methods are illustrated through an example involving cloud seeding. The coverage performance and interval width of each technique are then studied using simulation. A frequentist MATA interval performs best in the normal linear setting, while Bayesian credible intervals yield the best coverage performance in a lognormal setting. The use of a data-dependent prior probability for models improved the coverage of the model-averaged Bayesian interval, relative to that using uniform model prior probabilities. Data-dependent model prior probabilities are philosophically controversial in Bayesian statistics, and our results suggest that their use is beneficial when model averaging.
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institution Kabale University
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spelling doaj-art-504adfd02f514b008ec9e6bc78f320742025-02-03T00:59:14ZengWileyJournal of Probability and Statistics1687-952X1687-95382015-01-01201510.1155/2015/420483420483Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence IntervalsDaniel Turek0Department of Mathematics and Statistics, University of Otago, Dunedin 9054, New ZealandModel averaging is a technique used to account for model uncertainty, in both Bayesian and frequentist multimodel inferences. In this paper, we compare the performance of model-averaged Bayesian credible intervals and frequentist confidence intervals. Frequentist intervals are constructed according to the model-averaged tail area (MATA) methodology. Differences between the Bayesian and frequentist methods are illustrated through an example involving cloud seeding. The coverage performance and interval width of each technique are then studied using simulation. A frequentist MATA interval performs best in the normal linear setting, while Bayesian credible intervals yield the best coverage performance in a lognormal setting. The use of a data-dependent prior probability for models improved the coverage of the model-averaged Bayesian interval, relative to that using uniform model prior probabilities. Data-dependent model prior probabilities are philosophically controversial in Bayesian statistics, and our results suggest that their use is beneficial when model averaging.http://dx.doi.org/10.1155/2015/420483
spellingShingle Daniel Turek
Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals
Journal of Probability and Statistics
title Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals
title_full Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals
title_fullStr Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals
title_full_unstemmed Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals
title_short Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals
title_sort comparison of the frequentist mata confidence interval with bayesian model averaged confidence intervals
url http://dx.doi.org/10.1155/2015/420483
work_keys_str_mv AT danielturek comparisonofthefrequentistmataconfidenceintervalwithbayesianmodelaveragedconfidenceintervals