Consistency of an Information Criterion for High-Dimensional Multivariate Regression, 1st ed. 2019
JSS Research Series in Statistics Series

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Language: Anglais

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60 p. · 15.5x23.5 cm · Paperback

This is the first book on an evaluation of (weak) consistency of an information criterion for variable selection in high-dimensional multivariate linear regression models by using the high-dimensional asymptotic framework. It is an asymptotic framework such that the sample size n and the dimension of response variables vector p are approaching ? simultaneously under a condition that p/n goes to a constant included in [0,1).Most statistical textbooks evaluate consistency of an information criterion by using the large-sample asymptotic framework such that n goes to ? under the fixed p. The evaluation of consistency of an information criterion from the high-dimensional asymptotic framework provides new knowledge to us, e.g., Akaike's information criterion (AIC) sometimes becomes consistent under the high-dimensional asymptotic framework although it never has a consistency under the large-sample asymptotic framework; and Bayesian information criterion (BIC) sometimes becomes inconsistent under the high-dimensional asymptotic framework although it is always consistent under the large-sample asymptotic framework. The knowledge may help to choose an information criterion to be used for high-dimensional data analysis, which has been attracting the attention of many researchers.

1. Introduction.- 2. Information criteria in multivariate linear regression models.- 3.Several lemmas for proving consistency.- 4. Conditions to ensure consistency for AIC-type criterion under normality.- 5. Conditions to ensure consistency for AIC-type criterion under nonnormality.- 6. Conditions to ensure consistency of Cp-type criterion under normality.- 7. Conditions to ensure consistency of Cp-type criterion under nonnormality.- 8. Appendix.

Reevaluates the consistency of an information criterion by the high-dimensional asymptotic framework 

Deals with the high-dimensional asymptotic theory when the normality assumption is violated

Considers a wide class of information criteria