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Sieve estimation of Cox models with latent structures
- Cao, Yongxiu, Huang, Jian, Liu, Yanyan, Zhao, Xingqiu
- Biometrics 2016 v.72 no.4 pp. 1086-1097
- algorithms, biometry, models
- This article considers sieve estimation in the Cox model with an unknown regression structure based on right‐censored data. We propose a semiparametric pursuit method to simultaneously identify and estimate linear and nonparametric covariate effects based on B‐spline expansions through a penalized group selection method with concave penalties. We show that the estimators of the linear effects and the nonparametric component are consistent. Furthermore, we establish the asymptotic normality of the estimator of the linear effects. To compute the proposed estimators, we develop a modified blockwise majorization descent algorithm that is efficient and easy to implement. Simulation studies demonstrate that the proposed method performs well in finite sample situations. We also use the primary biliary cirrhosis data to illustrate its application.