Journal article
A multistage algorithm for best-subset model selection based on the Kullback–Leibler discrepancy
Computational statistics, Vol.31(2), pp.643-669
06/2016
DOI: 10.1007/s00180-015-0584-8
Abstract
The selection of a best-subset regression model from a candidate family is a common problem that arises in many analyses. The Akaike information criterion (AIC) and the corrected AIC (AICc) are frequently used for this purpose. AIC and AICc are designed to estimate the expected Kullback–Leibler discrepancy. For best-subset selection, both AIC and AICc are negatively biased, and the use of either criterion will lead to the selection of overfitted models. To correct for this bias, we introduce an “improved” AIC variant, AICi, which has a penalty term evaluated using Monte Carlo simulation. A multistage model selection procedure AICaps, which utilizes AICi, is proposed for best-subset selection. Simulation studies are compiled to compare the performances of the different model selection methods.
Details
- Title: Subtitle
- A multistage algorithm for best-subset model selection based on the Kullback–Leibler discrepancy
- Creators
- Tao ZhangJoseph E Cavanaugh
- Resource Type
- Journal article
- Publication Details
- Computational statistics, Vol.31(2), pp.643-669
- DOI
- 10.1007/s00180-015-0584-8
- ISSN
- 0943-4062
- eISSN
- 1613-9658
- Language
- English
- Date published
- 06/2016
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9983985833502771
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