Efficient Estimation for Semiparametric Structural Equation Models With Censored Data

• 成果类型: Article
• 署名作者: Wong, Kin Yau; Zeng, Donglin; Lin, D. Y.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1299626
• 发表日期: 2018
• 页码: 893-905
• 关键词: PROPORTIONAL HAZARDS MODEL; time-to-event; maximum-likelihood; social-interaction; regression-models; ASYMPTOTIC THEORY; identification; LIFE

摘要

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.