期刊
JOURNAL OF MULTIVARIATE ANALYSIS
卷 102, 期 9, 页码 1241-1255出版社
ELSEVIER INC
DOI: 10.1016/j.jmva.2011.04.007
关键词
Asymptotic robustness; Complete-case analysis; Conditional independence; Multi-sample analysis in SEM; Selection and pattern-mixture models; Shared-parameter model
资金
- Grants-in-Aid for Scientific Research [22300096, 23244011] Funding Source: KAKEN
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. (C) 2011 Elsevier Inc. All rights reserved.
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