Asymptotic distributions of two "synthetic data" estimators for censored single-index models

Xuewen Lu

Journal of Multivariate Analysis, 2010, vol. 101, issue 4, 999-1015

Abstract: The censored single-index model provides a flexible way for modelling the association between a response and a set of predictor variables when the response variable is randomly censored and the link function is unknown. It presents a technique for "dimension reduction" in semiparametric censored regression models and generalizes the existing accelerated failure time models for survival analysis. This paper proposes two methods for estimation of single-index models with randomly censored samples. We first transform the censored data into synthetic data or pseudo-responses unbiasedly, then obtain estimates of the index coefficients by the rOPG or rMAVE procedures of Xia (2006) [1]. Finally, we estimate the unknown nonparametric link function using techniques for univariate censored nonparametric regression. The estimators for the index coefficients are shown to be 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 the parameters are known. Monte Carlo simulations are conducted to illustrate the proposed methodologies.

Keywords: Accelerated; failure; time; model; Asymptotic; normality; rMAVE; rOPG; Random; censoring; Single-index; model; Synthetic; data (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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