  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.
