Moderate deviation principle for likelihood ratio test in multivariate linear regression model
type: Item
instance of: scholarly article
title: Moderate deviation principle for likelihood ratio test in multivariate linear regression model
author: Yanyan Li
published in: Journal of Multivariate Analysis
publication date: 2022-12-23T00:00:00Z
Mathematics Subject Classification ID: 62H15
Mathematics Subject Classification ID: 60F10
zbMATH DE Number: 7636968
zbMATH Keywords: high-dimensional data
zbMATH Keywords: likelihood ratio test
zbMATH Keywords: moderate deviation principle
zbMATH Keywords: multivariate linear regression
zbMATH Keywords: regression coefficient matrix
MaRDI profile type: Publication
full work available at URL: j.jmva.2022.105139
OpenAlex ID: W4311748738
cites work: Q4420195
cites work: Corrections to LRT on large-dimensional covariance matrix by RMT
cites work: Testing linear hypotheses in high-dimensional regressions
cites work: Q4882268
cites work: High-dimensional sparse MANOVA
cites work: A study of two high-dimensional likelihood ratio tests under alternative hypotheses
cites work: Delta method in large deviations and moderate deviations for estimators
cites work: Likelihood Ratio Test in Multivariate Linear Regression: from Low to High Dimension
cites work: On testing the equality of high dimensional mean vectors with unequal covariance matrices
cites work: Likelihood Ratio Tests for High‐Dimensional Normal Distributions
cites work: Moderate deviation principles for classical likelihood ratio tests of high-dimensional normal distributions
cites work: Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
cites work: On the distribution of the largest eigenvalue in principal components analysis
cites work: Moderate and Cramér-type large deviation theorems for M-estimators
cites work: Likelihood Ratio Test for Multi-Sample Mixture Model and Its Application to Genetic Imprinting
cites work: Limiting distributions of likelihood ratio test for independence of components for high-dimensional normal vectors
cites work: Multivariate analysis of variance with fewer observations than the dimension
cites work: Asymptotic normality and moderate deviation principle for high-dimensional likelihood ratio statistic on block compound symmetry covariance structure
cites work: Asymptotic Expansion of the Null Distribution of LR Statistic for Multivariate Linear Hypothesis when the Dimension is Large
cites work: Berry-Esseen bound for high dimensional asymptotic approximation of Wilks' lambda distribution
cites work: Edgeworth expansion of Wilks' lambda statistic
cites work: Asymptotic expansions of the distributions of MANOVA test statistics when the dimension is large
cites work: High-dimensional general linear hypothesis testing under heteroscedasticity
DOI: 10.1016/J.JMVA.2022.105139
description: scientific article; zbMATH DE number 7636968