Nonparametric Mean Preservation in Censored Regression
Using Preliminary Nonparametric Smoothing to Make Inference in Censored Regression
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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 depend...
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.
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.
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