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Efficient Estimation for Semiparametric Structural Equation Models With Censored Data.

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.

SUBMITTER: Wong KY

PROVIDER: S-EPMC6075718 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Efficient Estimation for Semiparametric Structural Equation Models With Censored Data.

Wong Kin Yau KY Zeng Donglin D Lin D Y DY

Journal of the American Statistical Association 20180606 522

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 consi ...[more]

PMID: 30083023

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