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 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.