Simple measures of uncertainty for model selection

Xiaohui Liu, Yuanyuan Li and Jiming Jiang ()
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Xiaohui Liu: Jiangxi University of Finance and Economics
Yuanyuan Li: University of California, Davis
Jiming Jiang: University of California, Davis

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2021, vol. 30, issue 3, No 7, 673-692

Abstract: Abstract We develop two simple measures of uncertainty for a model selection procedure. The first measure is similar in spirit to confidence set in parameter estimation; the second measure is focusing on error in model selection. The proposed methods are simpler, both conceptually and computationally, than the existing measures of uncertainty in model selection. We recognize major differences between model selection and traditional estimation or prediction problems, and propose reasonable frameworks, under which these measures are developed, and their theoretical properties are established. Empirical studies demonstrate performance of the proposed measures, their superiority over the existing methods, and their relevance to real-life applications.

Keywords: Average probability of coverage; Bootstrapping; Consistency; LogP measure; Model confidence set; Model selection; Uncertainty; 62A99 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11749-020-00737-9

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