Randomly censored partially linear single-index models

Xuewen Lu and Tsung-Lin Cheng

Journal of Multivariate Analysis, 2007, vol. 98, issue 10, 1895-1922

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.

Keywords: Accelerated; failure-time; model; Asymptotic; normality; Kernel; smoothing; Local; linear; fit; Partially; linear; single-index; model; Quasi; likelihood; Random; censoring; Synthetic; data (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (12)

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