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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/14068

Title: Randomly Censored Partially Linear Single-index Model
Authors: Lu, Xue-Wen;Cheng, Tsung-Lin
Contributors: 數學系
Keywords: Accelerated failure-time model;Asymptotic normality;Kernel smoothing;Local linear fit;Partially linear single-index model;Quasi likelihood;Random censoring;Synthetic data
Date: 2007-11
Issue Date: 2012-09-10T06:01:40Z
Publisher: Academic Press, Inc.
Abstract: 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.
Relation: Journal of Multivariate Analysis, 98(10): 1895-1922
Appears in Collections:[數學系] 期刊論文

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