Partially linear transformation model for length-biased and right-censored data

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
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Original languageEnglish
Pages (from-to)332-367
Journal / PublicationJournal of Nonparametric Statistics
Issue number2
Online published17 Jan 2018
Publication statusPublished - Apr 2018


In this paper, we consider a partially linear transformation model for data subject to length-biasedness and right-censoring which frequently arise simultaneously in biometrics and other fields. The partially linear transformation model can account for nonlinear covariate effects in addition to linear effects on survival time, and thus reconciles a major disadvantage of the popular semiparamnetric linear transformation model. We adopt local linear fitting technique and develop an unbiased global and local estimating equations approach for the estimation of unknown covariate effects. We provide an asymptotic justification for the proposed procedure, and develop an iterative computational algorithm for its practical implementation, and a bootstrap resampling procedure for estimating the standard errors of the estimator. A simulation study shows that the proposed method performs well in finite samples, and the proposed estimator is applied to analyse the Oscar data.

Research Area(s)

  • Estimating equations, length-biased sampling, local linear fitting technique, partially linear transformation model, right-censoring