×

Sieve estimation of Cox models with latent structures. (English) Zbl 1390.62238

Summary: 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.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62J02 General nonlinear regression
62N01 Censored data models
62G08 Nonparametric regression and quantile regression
PDFBibTeX XMLCite
Full Text: DOI