Testing equality of covariance matrices when data are incomplete

DOI10.1016/j.csda.2006.05.005zbMath1162.62369OpenAlexW1988076422MaRDI QIDQ1020078

Mortaza Jamshidian, James R. Schott

Publication date: 29 May 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.05.005

zbMATH Keywords

likelihood ratio testWald testmissing datamissing completely at randomrobust testsre-scaled likelihood ratio testtest of homogeneity of covariance matrices

Mathematics Subject Classification ID

Related Items (6)

Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data ⋮ Test for high-dimensional mean vector under missing observations ⋮ Near-exact distributions for the likelihood ratio test statistic to test equality of several variance-covariance matrices in elliptically contoured distributions ⋮ Data-driven sensitivity analysis to detect missing data mechanism with applications to structural equation modelling ⋮ Identifying variables responsible for data not missing at random ⋮ The simultaneous test of equality and circularity of several covariance matrices

Cites Work

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• Tests of homogeneity of means and covariance matrices for multivariate incomplete data
• A robust testing procedure for the equality of covariance matrices
• Robustness and efficiency properties of scatter matrices
• Testing the equality of variance-covariance matrices the robust way
• Asymptotic distributions in canonical correlation analysis and other multivariate procedures for nonnormal populations
• Bootstrap Critical Values for Testing Homogeneity of Covariance Matrices
• Some tests for the equality of covariance matrices

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