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      A generalization of Turnbull's estimator for nonparametric estimation of the conditional survival function with interval-censored data.

      1 ,
      Lifetime data analysis
      Springer Nature America, Inc

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          Abstract

          Simple nonparametric estimates of the conditional distribution of a response variable given a covariate are often useful for data exploration purposes or to help with the specification or validation of a parametric or semi-parametric regression model. In this paper we propose such an estimator in the case where the response variable is interval-censored and the covariate is continuous. Our approach consists in adding weights that depend on the covariate value in the self-consistency equation proposed by Turnbull (J R Stat Soc Ser B 38:290-295, 1976), which results in an estimator that is no more difficult to implement than Turnbull's estimator itself. We show the convergence of our algorithm and that our estimator reduces to the generalized Kaplan-Meier estimator (Beran, Nonparametric regression with randomly censored survival data, 1981) when the data are either complete or right-censored. We demonstrate by simulation that the estimator, bootstrap variance estimation and bandwidth selection (by rule of thumb or cross-validation) all perform well in finite samples. We illustrate the method by applying it to a dataset from a study on the incidence of HIV in a group of female sex workers from Kinshasa.

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          Author and article information

          Journal
          Lifetime Data Anal
          Lifetime data analysis
          Springer Nature America, Inc
          1572-9249
          1380-7870
          Apr 2011
          : 17
          : 2
          Affiliations
          [1 ] Department of Mathematics & Statistics, Sisitan & Blouchestan University, Zahedan, Iran. m-hossein.dehghan.1@ulaval.ca
          Article
          10.1007/s10985-010-9174-9
          20544280
          7b7e18fa-6499-4cf5-b98a-7346e9b3c63d
          History

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