Sieve maximum likelihood estimator for semiparametric regression models with current status data

• 成果类型: Article
• 署名作者: Xue, HQ; Lam, KF; Li, GY
• 署名单位: Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Hong Kong; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000000313
• 发表日期: 2004
• 页码: 346-356
• 关键词: additive hazards regression; efficient estimation; linear-regression; convergence-rates

摘要

In a randomized controlled clinical trial study where the response variable of interest is the time to occurrence of a certain event, it is often too expensive or even impossible to observe the exact time. However, the current status of the subject at a random time of inspection is much more natural, feasible, and practical in terms of cost-effectiveness. This article considers a semiparametric regression model that consists of parametric and nonparametric regression components. A sieve maximum likelihood estimator (MLE) is proposed to estimate the regression parameter, allowing exploration of the nonlinear relationship between a certain covariate and the response function. Asymptotic properties of the proposed sieve MLEs are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. Moreover, the estimators of the unknown parameters are asymptotically efficient and normally distributed, and the estimator of the nonparametric function has an optimal convergence rate. Simulation Studies were carried out to investigate the performance of the proposed method. For illustration purposes, the method is applied to a dataset from a study of the calcification of the hydrogel intraocular lenses, a complication of cataract treatment.