Moderate deviation principle for likelihood ratio test in multivariate linear regression model

DOI10.1016/j.jmva.2022.105139OpenAlexW4311748738MaRDI QIDQ2111071

Yanyan Li

Publication date: 23 December 2022

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2022.105139

zbMATH Keywords

likelihood ratio testregression coefficient matrixhigh-dimensional datamoderate deviation principlemultivariate linear regression

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15) Large deviations (60F10)

Cites Work

• Unnamed Item
• Unnamed Item
• Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
• High-dimensional sparse MANOVA
• On testing the equality of high dimensional mean vectors with unequal covariance matrices
• Delta method in large deviations and moderate deviations for estimators
• Multivariate analysis of variance with fewer observations than the dimension
• Edgeworth expansion of Wilks' lambda statistic
• Corrections to LRT on large-dimensional covariance matrix by RMT
• Moderate and Cramér-type large deviation theorems for M-estimators
• On the distribution of the largest eigenvalue in principal components analysis
• Asymptotic expansions of the distributions of MANOVA test statistics when the dimension is large
• Limiting distributions of likelihood ratio test for independence of components for high-dimensional normal vectors
• Moderate deviation principles for classical likelihood ratio tests of high-dimensional normal distributions
• High-dimensional general linear hypothesis testing under heteroscedasticity
• Berry-Esseen bound for high dimensional asymptotic approximation of Wilks' lambda distribution
• Testing linear hypotheses in high-dimensional regressions
• Asymptotic Expansion of the Null Distribution of LR Statistic for Multivariate Linear Hypothesis when the Dimension is Large
• Likelihood Ratio Tests for High‐Dimensional Normal Distributions
• A study of two high-dimensional likelihood ratio tests under alternative hypotheses
• Likelihood Ratio Test in Multivariate Linear Regression: from Low to High Dimension
• Asymptotic normality and moderate deviation principle for high-dimensional likelihood ratio statistic on block compound symmetry covariance structure
• Likelihood Ratio Test for Multi-Sample Mixture Model and Its Application to Genetic Imprinting

This page was built for publication: Moderate deviation principle for likelihood ratio test in multivariate linear regression model