Regression for partially observed variables and nonparametric quantiles of conditional probabilities

Details Regression for partially observed variables and nonparametric quantiles of conditional probabilities Regression for partially observed variables and nonparametric quantiles of conditional probabilities

2007-10-19

arxiv.org/abs/0710.3666v1

Mathematics

Statistics Theory

Submitted to the Statistics Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical Statistics (http://www.im

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Regression for partially observed variables and nonparametric quantiles of conditional probabilities does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Regression for partially observed variables and nonparametric quantiles of conditional probabilities, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Regression for partially observed variables and nonparametric quantiles of conditional probabilities will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-708483