Tests of homogeneity of means and covariance matrices for multivariate incomplete data

DOI10.1007/BF02295134zbMath1297.62233OpenAlexW2018468690MaRDI QIDQ463136

Peter M. Bentler, Kevin H. Kim

Publication date: 15 October 2014

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02295134

zbMATH Keywords

likelihood ratiomissing datageneralized least squaresmultivariate normal distribution

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (12)

Normal distribution based pseudo ML for missing data: with applications to mean and covariance structure analysis ⋮ A nonparametric test of missing completely at random for incomplete multivariate data ⋮ Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data ⋮ Score Test for Missing at Random or Not under Logistic Missingness Models ⋮ Bartlett correction to the likelihood ratio test for MCAR with two‐step monotone sample ⋮ How to test the missing data mechanism in a hidden Markov model ⋮ Optimal nonparametric testing of missing completely at random and its connections to compatibility ⋮ A unified empirical likelihood approach for testing MCAR and subsequent estimation ⋮ Missing data mechanisms and homogeneity of means and variances-covariances ⋮ Data-driven sensitivity analysis to detect missing data mechanism with applications to structural equation modelling ⋮ Testing equality of covariance matrices when data are incomplete ⋮ Identifying variables responsible for data not missing at random

Uses Software

• AS 70
• EQS
• BMDP

Cites Work

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• On structural equation modeling with data that are not missing completely at random
• Theory and method for constrained estimation in structural equation models with incomplete data.
• Covariance structure analysis in several populations
• On some test criteria for covariance matrix
• Multiple population covariance structure analysis under arbitrary distribution theory
• A test of missing completely at random for generalised estimating equations with missing data
• An Asymptotic | chi 2 Test for the Equality of Two Correlation Matrices

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