Regression for partially observed variables and nonparametric quantiles of conditional probabilities
| dc.creator | Pons, Odile | |
| dc.date | 2007-10-19 | |
| dc.date.accessioned | 2026-07-07T08:37:19Z | |
| dc.date.available | 2026-07-07T08:37:19Z | |
| dc.description | Efficient estimation under bias sampling, censoring or truncation is a difficult question which has been partially answered and the usual estimators are not always consistent. Several biased designs are considered for models with variables $(X,Y)$ where $Y$ is an indicator and $X$ an explanatory variable, or for continuous variables $(X,Y)$. The identifiability of the models are discussed. New nonparametric estimators of the regression functions and conditional quantiles are proposed. | |
| dc.description | Submitted to the Statistics Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical Statistics (http://www.imstat.org) | |
| dc.identifier | https://arxiv.org/abs/0710.3666 | |
| dc.identifier | http://arxiv.org/abs/0710.3666 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/140361 | |
| dc.subject | Statistics Theory | |
| dc.subject | 62E10, 62G20, 62F10 (Primary) | |
| dc.title | Regression for partially observed variables and nonparametric quantiles of conditional probabilities | |
| dc.type | text |