Hypothesis testing for mean vector and covariance matrix of multi-populations under a two-step monotone incomplete sample in large sample and dimension

DOI10.1016/J.JMVA.2023.105290MaRDI QIDQ6536686

Shin-ichi Tsukada

Publication date: 13 May 2024

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

zbMATH Keywords

likelihood ratio criterionlarge dimensionequivalence of mean vectors and covariance matrices

Mathematics Subject Classification ID

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Cites Work

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• Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
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• Finite-sample inference with monotone incomplete multivariate normal data. II
• Two-sample inference for normal mean vectors based on monotone missing data
• Maximum-likelihood estimation of the parameters of a multivariate normal distribution
• On the likelihood ratio test for the equality of multivariate normal populations with two-step monotone missing data
• Multivariate data with missing observations
• Some Basic Properties of the Mle's for a Multivariate Normal Distribution with Monotone Missing Data
• Inferences on a normal covariance matrix and generalized variance with monotone missing data

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