文档介绍：This page intentionally left blank
Semiparametric Regression
Semiparametric regression is concerned with the flexible incorporation of nonlinear
functional relationships in regression analyses. Any application area that uses re-
gression analysis can benefit from semiparametric regression. Assuming only a basic
familiarity with ordinary parametric regression, this user-friendly book explains the
techniques and benefits of semiparametric regression in a concise and modular fash-
ion. The authors make liberal use of graphics and examples plus case studies taken
from environmental, financial, and other applications. They include practical advice
on implementation and pointers to relevant software.
This book is suitable as a textbook for students with little background in regression
as well as a reference book for statistically oriented scientists – such as biostatisti-
cians, econometricians, quantitative social scientists, and epidemiologists – with a
good working knowledge of regression and the desire to begin using more flexi-
ble semiparametric models. Even experts on semiparametric regression should find
something new here.
David Ruppert is the Andrew Schultz, Jr., Professor of Engineering (School of Op-
erations Research and Industrial Engineering) and Professor of Statistical Science at
Cornell University. He has served as editor for a number of prestigious series and
journals and has published some 80 articles of his own as well as co-authoring two
popular books, Transformation and Weighting in Regression and Measurement Error
in Nonlinear Models. He is also winner of the Wilcoxon Prize for best practical ap-
plications paper in technometrics and an elected Fellow of the American Statistical
Association and the Institute of Mathematical Statistics.
M. P. Wand is Professor of Statistics at the University of New South Wales in Sydney,
Australia. He has held faculty appointments at Harvard University, Rice University,
and