Partially Linear Models (Contributions to Statistics)

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, non-linear and nonparametric time series models.
1 Introduction.- 2 Estimation of The Parametric Component.- 3 Estimation of The Nonparametric Component.- 4 Estimation with Measurement Errors.- 5 Some Related Theoretic Topics.- 6 Partially Linear Time Series Models.- Appendix: Basic Lemmas.- Author Index.- Symbols and Notation. Wolfgang Härdle is a professor of statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. the Centre for Applied Statistics and Economics. He teaches quantitative finance and semiparametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected ISI member and advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan.