Buckley–James-type of estimators under the classical case cohort design |
| |
Authors: | Qiqing Yu George Y C Wong Menggang Yu |
| |
Institution: | (1) Department of Mathematical Sciences, SUNY, Binghamton, NY 13902, USA;(2) Strang Cancer Prevention Center, 428 E 72nd Street, New York, NY 10021, USA;(3) Department of Medicine/Biostatistics, Indiana University, Indianapolis, IN 46202, USA |
| |
Abstract: | We consider the estimation problem with classical case-cohort data. The case-cohort design was first proposed by Prentice
(Biometrics 73:1–11, 1986). Most studies focus on the Cox regression model. In this paper, we consider the linear regression
model. We propose an estimator which extends the Buckley–James estimator to the classical case-cohort design. In order to
derive the BJE, there is an additional problem of finding the generalized maximum likelihood estimator (GMLE) of the underlying
distribution functions. We propose a self-consistent algorithm for the GMLE. We also justify that the GMLE is consistent and
asymptotically normally distributed under certain regularity conditions. We further present some simulation results on the
asymptotic properties of the BJE and apply our procedure to a data set used in the literature. |
| |
Keywords: | Case-cohort study Buckley– James estimator Right-censorship Linear regression model Self-consistent algorithm Survival data |
本文献已被 SpringerLink 等数据库收录! |
|