Likelihood ratio tests under model misspecification in high dimensions

Nina Dörnemann

Journal of Multivariate Analysis, 2023, vol. 193, issue C

Abstract: We investigate the likelihood ratio test for a large block-diagonal covariance matrix with an increasing number of blocks under the null hypothesis. While so far the likelihood ratio statistic has only been studied for normal populations, we establish that its asymptotic behavior is invariant under a much larger class of distributions. This implies robustness against model misspecification, which is common in high-dimensional regimes. Demonstrating the flexibility of our approach, we additionally establish asymptotic normality of the log-likelihood ratio test for the equality of many large sample covariance matrices under model uncertainty. A simulation study and an analysis of a data set from psychology emphasize the usefulness of our findings.

Keywords: Block-diagonal covariance matrix; Central limit theorem; Equality of covariance matrices; High-dimensional inference; Likelihood ratio test; Model misspecification; Non-normal population (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.jmva.2022.105122

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