文档介绍：PARTIALLY LINEAR MODELS
Wolfgang H¨ardle
Institut f¨urStatistik und Okonometrie¨
Humboldt-Universit¨atzu Berlin
D-10178 Berlin, Germany
Hua Liang
Department of Statistics
Texas A&M University
College Station
TX 77843-3143, USA
and
Institut f¨urStatistik und Okonometrie¨
Humboldt-Universit¨atzu Berlin
D-10178 Berlin, Germany
Jiti Gao
School of Mathematical Sciences
Queensland University of Technology
Brisbane QLD 4001, Australia
and
Department of Mathematics and Statistics
The University of Western Australia
Perth WA 6907, Australia
ii
In the last ten years, there has been increasing interest and activity in the
general area of partially linear regression smoothing in statistics. Many methods
and techniques have been proposed and studied. This monograph hopes to bring
an up-to-date presentation of the state of the art of partially linear regression
techniques. The emphasis of this monograph is on methodologies rather than on
the theory, with a particular focus on applications of partially linear regression
techniques to various statistical problems. These problems include least squares
regression, asymptotically efficient estimation, bootstrap resampling, censored
data analysis, linear measurement error models, nonlinear measurement models,
nonlinear and nonparametric time series models.
We hope that this monograph will serve as a useful reference for theoretical
and applied statisticians and to graduate students and others who are interested
in the area of partially linear regression. While advanced mathematical ideas
have been valuable in some of the theoretical development, the methodological
power of partially linear regression can be demonstrated and discussed without
advanced mathematics.
This monograph can be divided into three parts: part one–Chapter 1 through
Chapter 4; part two–Chapter 5; and part three–Chapter 6. In the first part, we
discuss various estimators for partially linear regression mod