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Abstract:
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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.
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