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

Yutaka Kano and Keiji Takai

Journal of Multivariate Analysis, 2011, vol. 102, issue 9, 1241-1255

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.

Keywords: Asymptotic; robustness; Complete-case; analysis; Conditional; independence; Multi-sample; analysis; in; SEM; Selection; and; pattern-mixture; models; Shared-parameter; model (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (2)

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