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

ISBN-13: 9783836463911 / Angielski / Miękka / 2009 / 180 str.

Cédric Heuchenne

Opis

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.

O autorze

Cédric Heuchenne is Professor of Statistics at HEC Management
School-University of Liège. He holds a Master's degree in
Applied Sciences and a Ph.D. in Statistics from the Catholic
University of Louvain. His research interests focus on
nonparametric statistical inference for complex data structures.