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Activity Number: 542
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #319471 View Presentation
Title: Semiparametric Structural Equation Models with Latent Variables for Right-Censored Data
Author(s): Kin Yau Wong* and Donglin Zeng and Danyu Lin
Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Keywords: Integrative analysis ; Latent variables ; Model identifiability ; Multivariate analysis ; Nonparametric maximum likelihood estimation ; Survival analysis

Structural equation modeling is commonly used to capture complex structures of relationships among multiple variables, both latent and observed. In this paper, 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 computation. 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 application to a motivating cancer study that contains a variety of genomic variables.

Authors who are presenting talks have a * after their name.

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