Randomly censored partially linear single-index models

Xuewen Lu, Tsung Lin Cheng

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1895-1922
Number of pages28
JournalJournal of Multivariate Analysis
Issue number10
Publication statusPublished - 2007 Nov 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Randomly censored partially linear single-index models'. Together they form a unique fingerprint.

Cite this