Abstract：
: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.
journal_name
Biometricsjournal_title
Biometricsauthors
Cao Y,Huang J,Liu Y,Zhao Xdoi
10.1111/biom.12529subject
Has Abstractpub_date
2016-12-01 00:00:00pages
1086-1097issue
4eissn
0006-341Xissn
1541-0420journal_volume
72pub_type
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