Normal distribution based pseudo ML for missing data: with applications to mean and covariance structure analysis

DOI10.1016/j.jmva.2009.05.001zbMath1170.62040OpenAlexW2087023496MaRDI QIDQ842909

Ke-Hai Yuan

Publication date: 28 September 2009

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2009.05.001

zbMATH Keywords

consistencyasymptotic biasfactor analysisestimating equationsandwich-type covariance matrixnot missing at random

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Applications of statistics to psychology (62P15)

Related Items

Consistency, bias and efficiency of the normal-distribution-based MLE: the role of auxiliary variables ⋮ Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data ⋮ On the use of the selection matrix in the maximum likelihood estimation of normal distribution models with missing data ⋮ Analysis of NMAR missing data without specifying missing-data mechanisms in a linear latent variate model ⋮ Missing data mechanisms and homogeneity of means and variances-covariances ⋮ Effect of Violation of the Normal Assumption on MI and ML Estimators in the Analysis of Incomplete Data ⋮ Asymptotic Inference with Incomplete Data

Cites Work

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• Longitudinal data analysis using generalized linear models
• Analysis of structural equation model with ignorable missing continuous and polytomous data
• Tests of homogeneity of means and covariance matrices for multivariate incomplete data
• Estimation for structural equation models with missing data
• 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.
• Estimation for the multiple factor model when data are missing
• Asymptotics of estimating equations under natural conditions.
• A theorem on uniform convergence of stochastic functions with applications
• ML estimation of the multivariate \(t\) distribution and the EM algorithm
• Likelihood based frequentist inference when data are missing at random
• Maximum Likelihood Estimates for a Multivariate Normal Distribution when some Observations are Missing
• Missing Data in Longitudinal Studies
• Every Missingness not at Random Model Has a Missingness at Random Counterpart with Equal Fit
• Pseudo Maximum Likelihood Methods: Theory
• Robust Estimation of the Mean and Covariance Matrix from Data with Missing Values
• Inference and missing data
• Sample Selection Bias as a Specification Error
• Regression Analysis when the Dependent Variable Is Truncated Normal
• Asymptotic Properties of Non-Linear Least Squares Estimators
• Estimation of Parameters from Incomplete Data
• Maximum Likelihood Estimation of Misspecified Models