|Title||Efficient Estimation for Semiparametric Structural Equation Models With Censored Data.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Wong, Kin Yau, Donglin Zeng, and D Y. Lin|
|Journal||J Am Stat Assoc|
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.
|Alternate Journal||J Am Stat Assoc|
|Original Publication||Efficient estimation for semiparametric structural equation models with censored data.|
|PubMed Central ID||PMC6075718|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA082659 / CA / NCI NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
Efficient Estimation for Semiparametric Structural Equation Models With Censored Data.