Efficient Estimation for Semiparametric Structural Equation Models With Censored Data

Kin Yau Wong, Donglin Zeng, D. Y. Lin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Structural equation modeling is commonly used to capture complex structures of relationships among multiple variables, both latent and observed. We propose a general class of structural equation models with a semiparametric component for potentially censored survival times. We consider nonparametric maximum likelihood estimation and devise a combined expectation-maximization and Newton-Raphson algorithm for its implementation. We establish conditions for model identifiability and prove the consistency, asymptotic normality, and semiparametric efficiency of the estimators. Finally, we demonstrate the satisfactory performance of the proposed methods through simulation studies and provide an application to a motivating cancer study that contains a variety of genomic variables. Supplementary materials for this article are available online.

Original languageEnglish
Pages (from-to)893-905
Number of pages13
JournalJournal of the American Statistical Association
Volume113
Issue number522
DOIs
Publication statusPublished - 3 Apr 2018
Externally publishedYes

Keywords

  • Integrative analysis
  • Joint modeling
  • Latent variables
  • Model identifiability
  • Nonparametric maximum likelihood estimation
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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