Conditions for Consistency of a Log-Likelihood-Based Information Criterion in Normal Multivariate Linear Regression Models under the Violation of the Normality Assumption

In this paper, we clarify conditions for consistency of a log-likelihood-based information criterion in multivariate linear regression models with a normality assumption. Although a normality is assumed to the distribution of the candidate model, we frame the situation as that the assumption of normality may be violated. The conditions for consistency are derived from two types of asymptotic theory; one is based on a large-sample asymptotic framework in which only the sample size approaches∞, and the other is based on a high-dimensional asymptotic framework in which the sample size and the dimension of the vector of response variables simultaneously approach ∞. In both cases, our results are free of the influence of nonnormality in the true distribution.