Efficient Estimation for Semiparametric Structural Equation Models With Censored Data
提出了一类带删失生存时间的半参数结构方程模型，用非参数最大似然估计和EM-Newton-Raphson混合算法求解，证明了可识别性和估计量的渐近性质，并通过模拟和癌症基因组数据验证了方法有效性。
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.