Journal article
Extending AIC to best subset regression
Computational Statistics, Vol.33(2), pp.787-806
06/2018
DOI: 10.1007/s00180-018-0797-8
Abstract
The Akaike information criterion (AIC) is routinely used for model selection in best subset regression. The standard AIC, however, generally under-penalizes model complexity in the best subset regression setting, potentially leading to grossly overfit models. Recently, Zhang and Cavanaugh (Comput Stat 31(2):643–669, 2015) made significant progress towards addressing this problem by introducing an effective multistage model selection procedure. In this paper, we present a rigorous and coherent conceptual framework for extending AIC to best subset regression. A new model selection algorithm derived from our framework possesses well understood and desirable asymptotic properties and consistently outperforms the procedure of Zhang and Cavanaugh in simulation studies. It provides an effective tool for combating the pervasive overfitting that detrimentally impacts best subset regression analysis so that the selected models contain fewer irrelevant predictors and predict future observations more accurately.
Details
- Title: Subtitle
- Extending AIC to best subset regression
- Creators
- J Liao - 0000 0001 2097 4281 grid.29857.31 Penn State University Hershey PA USAJoseph Cavanaugh - 0000 0004 1936 8294 grid.214572.7 University of Iowa Iowa City IA USATimothy McMurry - 0000 0000 9136 933X grid.27755.32 University of Virginia Charlottesville VA USA
- Resource Type
- Journal article
- Publication Details
- Computational Statistics, Vol.33(2), pp.787-806
- DOI
- 10.1007/s00180-018-0797-8
- ISSN
- 0943-4062
- eISSN
- 1613-9658
- Publisher
- Springer Berlin Heidelberg; Berlin/Heidelberg
- Language
- English
- Date published
- 06/2018
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9983986088702771
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