Asymptotic normality and moderate deviation principle for high-dimensional likelihood ratio statistic on block compound symmetry covariance structure

DOI10.1080/02331888.2020.1715408zbMath1437.62201OpenAlexW3002230054WikidataQ126304597 ScholiaQ126304597MaRDI QIDQ5213360

Gaoming Sun, Junshan Xie

Publication date: 3 February 2020

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2020.1715408

zbMATH Keywords

asymptotic normalitylikelihood ratio testhigh-dimensional datamoderate deviation principleBCS covariance structure

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05)

Related Items (3)

The moderate deviation principles of likelihood ratio tests under alternative hypothesis ⋮ Testing independence under a block compound symmetry covariance structure ⋮ Moderate deviation principle for likelihood ratio test in multivariate linear regression model

Cites Work

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• Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
• Linear discrimination with equicorrelated training vectors
• Optimal estimation for doubly multivariate data in blocked compound symmetric covariance structure
• High-dimensional asymptotic expansion of LR statistic for testing intraclass correlation structure and its error bound
• Moderate and Cramér-type large deviation theorems for M-estimators
• Testing of multivariate repeated measures data with block exchangeable covariance structure
• Testing the hypothesis of a block compound symmetric covariance matrix for elliptically contoured distributions
• Testing the equality of mean vectors for paired doubly multivariate observations in blocked compound symmetric covariance matrix setup
• Moderate deviation principles for classical likelihood ratio tests of high-dimensional normal distributions
• The likelihood ratio test for a separable covariance matrix
• Linear Models with Doubly Exchangeable Distributed Errors
• Estimating and Testing a Structured Covariance Matrix for Three-Level Multivariate Data
• Estimation of interclass correlations in familial data
• Likelihood Ratio Tests for High‐Dimensional Normal Distributions
• Linear Models with Exchangeably Distributed Errors
• Multivariate Repeated-Measurement or Growth Curve Models with Multivariate Random-Effects Covariance Structure
• Testing and estimation when a normal covariance matrix has intraclass structure of arbitrary order
• Probability: A Graduate Course
• Testing a block exchangeable covariance matrix
• A GENERAL DISTRIBUTION THEORY FOR A CLASS OF LIKELIHOOD CRITERIA
• Sample Criteria for Testing Equality of Means, Equality of Variances, and Equality of Covariances in a Normal Multivariate Distribution
• Multivariate Statistics
• The Estimation of Intraclass Correlation in the Analysis of Family Data
• Application of the theory of products of problems to certain patterned covariance matrices

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