Regression for partially observed variables and nonparametric quantiles of conditional probabilities

Odile Pons

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arXiv · 0710.3666

Regression for partially observed variables and nonparametric quantiles of conditional probabilities

Odile Pons

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Abstract

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.

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Odile Pons. 2007-10-19. Regression for partially observed variables and nonparametric quantiles of conditional probabilities. https://arxiv.org/abs/0710.3666

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