Testing hypotheses about covariance matrices using bootstrap methods

DOI10.1080/03610929308831051zbMath0798.62070OpenAlexW2033855608MaRDI QIDQ4275798

Ji Zhang, Dennis D. Boos

Publication date: 8 November 1994

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929308831051

zbMATH Keywords

linear transformationpooled samplesbootstrap proceduresoptimal proceduresseparate samplestesting covariance structures

Mathematics Subject Classification ID

Hypothesis testing in multivariate analysis (62H15)

Related Items (8)

Testing hypotheses about covariance matrices in general MANOVA designs ⋮ Comparing latent means without mean structure models: a projection-based approach ⋮ Tie-respecting bootstrap methods for estimating distributions of sets and functions of eigenvalues ⋮ A new method for multi-sample high-dimensional covariance matrices test based on permutation ⋮ Checking the adequacy of the multivariate semiparametric location shift model ⋮ Tests of covariance matrix by using projection pursuit and bootstrap method ⋮ The effects of nonnormality on asymptotic distributions of some likelihood ratio criteria for testing covariance structures under normal assumption ⋮ Discussion about the quality of F-ratio resampling tests for comparing variances

Cites Work

• Some contributions to efficient statistics in structural models: Specification and estimation of moment structures
• Bootstrap tests and confidence regions for functions of a covariance matrix
• Proportionaliy of covariance matrices
• Resampling from centered data in the two-sample problem
• Covariance structure analysis in several populations
• Structural analysis of covariance and correlation matrices
• Multiple population covariance structure analysis under arbitrary distribution theory
• Approximation Theorems of Mathematical Statistics
• Bootstrap Methods for Testing Homogeneity of Variances
• Bootstrap Critical Values for Testing Homogeneity of Covariance Matrices
• Investigations on the Similarity of the Structure of Two Covariance Matrices for Some Blood Pressure Data

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