Nonparametric Mean Preservation in Censored Regression

The aim of this book is to estimate the conditional
mean of some functions depending on the response
variable Y (moments, distributions...) in regression
models where this response is possibly censored. In
parametric regression, polynomial and nonlinear
conditional means are estimated in a new way while,
in nonparametric regression, some new estimators are
provided to approximate general L-functionals
(conditional mean, trimmed mean, quantiles...). The
ideas developed in those methods lead to establish
more general results in nonparametric estimation of
the conditional mean of functions depending on Y and
other variables and where the response can follow
other schemes of incomplete data (not only censored
but also missing or length-biased data). For each
procedure, asymptotic properties are established
while finite sample behavior is studied via
simulations. Examples from a variety of areas
highlight the interest of using the proposed
methodologies in practice.