The weighted average information criterion for multivariate regression model selection

Authors:
Tiee-Jian Wu;Pinyuen Chen;Yanjun Yan
Affiliations:
Department of Statistics, National Cheng-Kung University, Tainan 70101, Taiwan;Department of Mathematics, Syracuse University, Syracuse, NY 13244, USA;ARCON Corp, Waltham, MA 02451, USA
Venue:
Signal Processing
Year:
2013

Citing 5
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Abstract

We propose a consistent criterion WIC"v"c (vector corrected weighed average information criterion) for model order selection in multivariate linear regression models. The WIC"v"c is a weighted average of the asymptotically efficient criterion KIC"v"c (vector corrected Kullback information criterion) and the consistent criterion MBIC (multivariate Bayesian information criterion). The WIC"v"c behaves like KIC"v"c in small samples and behaves like MBIC in large samples. A numerical study comparing the performance of the proposed criterion with several available model selection criteria has been done. It shows that, over a wide range of small, moderate and large sample sizes, the WIC"v"c is more stable in comparison to other criteria in the study; that is, the WIC"v"c is either as good or comes in a strong second, whereas other criteria vary more in performance ranking. Therefore, the WIC"v"c is a very reliable and practical criterion.