National Changhua University of Education Institutional Repository : Item 987654321/14068
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题名: Randomly Censored Partially Linear Single-index Model
作者: Lu, Xue-Wen;Cheng, Tsung-Lin
贡献者: 數學系
关键词: Accelerated failure-time model;Asymptotic normality;Kernel smoothing;Local linear fit;Partially linear single-index model;Quasi likelihood;Random censoring;Synthetic data
日期: 2007-11
上传时间: 2012-09-10T06:01:40Z
出版者: Academic Press, Inc.
摘要: This paper proposes a method for estimation of a class of partially linear single-index models with randomly censored samples. The method provides a flexible way for modelling the association between a response and a set of predictor variables when the response variable is randomly censored. It presents a technique for ''dimension reduction'' in semiparametric censored regression models and generalizes the existing accelerated failure-time models for survival analysis. The estimation procedure involves three stages: first, transform the censored data into synthetic data or pseudo-responses unbiasedly; second, obtain quasi-likelihood estimates of the regression coefficients in both linear and single-index components by an iteratively algorithm; finally, estimate the unknown nonparametric regression function using techniques for univariate censored nonparametric regression. The estimators for the regression coefficients are shown to be jointly root-n consistent and asymptotically normal. In addition, the estimator for the unknown regression function is a local linear kernel regression estimator and can be estimated with the same efficiency as all the parameters are known. Monte Carlo simulations are conducted to illustrate the proposed methodology.
關聯: Journal of Multivariate Analysis, 98(10): 1895-1922
显示于类别:[數學系] 期刊論文

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