Journal article
Bootstrap variants of the Akaike information criterion for mixed model selection
Computational statistics & data analysis, Vol.52(4), pp.2004-2021
2008
DOI: 10.1016/j.csda.2007.06.019
Abstract
Two bootstrap-corrected variants of the Akaike information criterion are proposed for the purpose of small-sample mixed model selection. These two variants are asymptotically equivalent, and provide asymptotically unbiased estimators of the expected Kullback–Leibler discrepancy between the true model and a fitted candidate model. The performance of the criteria is investigated in a simulation study where the random effects and the errors for the true model are generated from a Gaussian distribution. The parametric bootstrap is employed. The simulation results suggest that both criteria provide effective tools for choosing a mixed model with an appropriate mean and covariance structure. A theoretical asymptotic justification for the variants is presented in the Appendix.
Details
- Title: Subtitle
- Bootstrap variants of the Akaike information criterion for mixed model selection
- Creators
- Junfeng Shang - Department of Mathematics and Statistics, 450 Math Science Building, Bowling Green State University, Bowling Green, OH 43403, USAJoseph E Cavanaugh - Department of Biostatistics, The University of Iowa, IA 52242, USA
- Resource Type
- Journal article
- Publication Details
- Computational statistics & data analysis, Vol.52(4), pp.2004-2021
- DOI
- 10.1016/j.csda.2007.06.019
- ISSN
- 0167-9473
- eISSN
- 1872-7352
- Publisher
- Elsevier B.V
- Language
- English
- Date published
- 2008
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9984214662702771
Metrics
17 Record Views