2013/10/22 by Sandrine Casanova, Casanova, Sandrine, Eve Leconte +1
Economics, Econometrics and Finance · Mathematics · #Economic and Environmental Valuation #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Survey Sampling and Estimation Techniques #stat.ME
paper · pdf · doi:10.48550/arxiv.1310.5927
18 pages, 5 figures
openalex publication_date 2013/10/22 · arxiv created 2014/04/11 · arxiv updated 2014/04/14 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
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 computed with 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.