Analysis of NMAR missing data without specifying missing-data mechanisms in a linear latent variate model

Authors:
Yutaka Kano;Keiji Takai
Affiliations:
-;-
Venue:
Journal of Multivariate Analysis
Year:
2011

Citing 4
Cited 1

Statistical analysis with missing data

Statistical analysis with missing data
Model conditions for asymptotic robustness in the analysis of linear relations

Computational Statistics & Data Analysis
Asymptotics of estimating equations under natural conditions

Journal of Multivariate Analysis
Normal distribution based pseudo ML for missing data: With applications to mean and covariance structure analysis

Journal of Multivariate Analysis

Consistency, bias and efficiency of the normal-distribution-based MLE: The role of auxiliary variables

Journal of Multivariate Analysis

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Abstract

It is natural to assume that a missing-data mechanism depends on latent variables in the analysis of incomplete data in latent variate modeling because latent variables are error-free and represent key notions investigated by applied researchers. Unfortunately, the missing-data mechanism is then not missing at random (NMAR). In this article, a new estimation method is proposed, which leads to consistent and asymptotically normal estimators for all parameters in a linear latent variate model, where the missing mechanism depends on the latent variables and no concrete functional form for the missing-data mechanism is used in estimation. The method to be proposed is a type of multi-sample analysis with or without mean structures, and hence, it is easy to implement. Complete-case analysis is shown to produce consistent estimators for some important parameters in the model.