@techreport{publications15887,
          volume = {14-487},
           month = {April},
          author = {Sandrine Casanova and Eve Leconte},
          series = {TSE Working Paper},
       booktitle = {TSE Working Paper},
           title = {A nonparametric model-based estimator for the cumulative distribution function of a right censored variable in a finite population},
            type = {Working Paper},
       publisher = {TSE Working Paper},
            year = {2014},
     institution = {Universit{\'e} Toulouse 1 Capitole},
        keywords = {Cumulative distribution function, auxiliary information, censored data, generalized Kaplan-Meier estimator, nonparametric conditional median, bootstrap estimation},
             url = {https://publications.ut-capitole.fr/id/eprint/15887/},
        abstract = {In survey analysis, the estimation of the cumulative distribution function (cdf) is of great interest: it allows for instance to derive quantiles estimators or other non linear parameters derived from the cdf. We consider the case where the response variable is a right censored duration variable. In this framework, the classical estimator of the cdf is the Kaplan-Meier estimator. As an alternative, we propose a nonparametric model-based estimator of the cdf in a finite population. The new estimator uses auxiliary information brought by a continuous covariate and is based on nonparametric median regression adapted to the censored case. The bias and variance of the prediction error of the estimator are estimated by a bootstrap procedure adapted to censoring. The new estimator is compared by model-based simulations to the Kaplan-Meier estimator computedwith the sampled individuals: a significant gain in precision is brought by the new method whatever the size of the sample and the censoring rate. Welfare duration data are used to illustrate the new methodology.}
}