Casanova, Sandrine and Leconte, Eve (2014) A nonparametric model-based estimator for the cumulative distribution function of a right censored variable in a finite population. TSE Working Paper, n. 14-487

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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.

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• A nonparametric model-based estimator for the cumulative distribution function of a right censored variable in a finite population. (deposited 09 Jul 2014 17:43) [Currently Displayed]
  • A nonparametric model-based estimator for the cumulative distribution function of a right censored variable in a finite population. (deposited 15 Apr 2016 14:38)