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.

Symbols and Notation.- Introduction: Background, History and Practical Examples.- The Least Squares Estimators.- Assumptions and Remarks.- The Scope of the Monograph.- The Structure of the Monograoh.- Estimation of the Parametric Component: Estimation with Heteroscedastic Errors.- Estimation with Censored Data.- Bootstrap Approximations.- Estimation of the Nonparametric Component: Introduction.- Consistency Results.- Asymptotic Normality.- Simulated and Real Examples.- Appendix.- Estimation with Measurement Errors: Linear Variables with Measurement Errors.- Nonlinear Variables with Measurement Errors.- Some Related Theoretic Topics: The Laws of the Iterated Logarithm.- The Berry-Esseen Bounds.- Asymptotically Efficient Estimation.- Bahadur Asymptotic Efficiency.- Second Order Asymptotic Efficiency.- Estimation of the Error Distribution.- Partially Linear Time Series Models: Introduction.- Adaptive Parametric and Nonparametric Tests.- Optimum Linear Subset Selection.- Optimum Bandwidth Selection.- Other Related Developments.- The Assumptions and the Proofs of Theorems.- Appendix: Basic Lemmas.