A Nonparametric Model-Based Estimator for the Cumulative Distribution Function of a Right-Censored Variable in a Finite Population
| Autor: | Sandrine Casanova, Eve Leconte |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Statistics::Theory Hodges–Lehmann estimator Applied Mathematics Estimator Trimmed estimator Minimum-variance unbiased estimator Efficient estimator Bias of an estimator Nelson–Aalen estimator Statistics Consistent estimator Econometrics Statistics::Methodology Statistics Probability and Uncertainty Social Sciences (miscellaneous) Mathematics |
| Zdroj: | Journal of Survey Statistics and Methodology. 3:317-338 |
| ISSN: | 2325-0992 2325-0984 |
| DOI: | 10.1093/jssam/smv006 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|