Efficient Estimation for Semiparametric Structural Equation Models With Censored Data.

TitleEfficient Estimation for Semiparametric Structural Equation Models With Censored Data.
Publication TypeJournal Article
Year of Publication2018
AuthorsWong, Kin Yau, Donglin Zeng, and D Y. Lin
JournalJ Am Stat Assoc
Volume113
Issue522
Pagination893-905
Date Published2018
ISSN0162-1459
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.

DOI10.1080/01621459.2017.1299626
Alternate JournalJ Am Stat Assoc
Original PublicationEfficient estimation for semiparametric structural equation models with censored data.
PubMed ID30083023
PubMed Central IDPMC6075718
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA082659 / CA / NCI NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
Project: