A bias reduction theorem with applications in nonparametric regression /

Bibliographic Details
Main Authors:	Eubank, Randall L., 1952- (Author), Speckman, Paul L. (Paul Lorenz), 1946- (Author)
Format:	Book
Language:	English
Published:	College Station, Texas : Department of Statistics, Texas A & M University, [1989]
Series:	Technical report (Texas A & M University. Department of Statistics) ; no. 53.
Subjects:	Parameter estimation. Regression analysis. Nonparametric statistics. Estimation theory. Fourier series. Kernel functions. Spline theory. Mathematical statistics. Statistics > Nonparametric inference > Nonparametric regression. Partially linear models Rates of convergence Trigonometric series estimators Smoothing splines

Description
Abstract:	A general approach is derived for reducing the bias of estimators in a parameter estimation problem. The technique is particularly well suited for certain types of bias that arise in nonparametric regression analysis. This is illustrated by deriving new methods for the removal of edge effects from kernel regression, smoothing spline and trigonometric series estimators. An application to estimation in partially linear semiparametric models is also presented.
Physical Description:	16 leaves, 6 unnumbered leaves : illustrations ; 28 cm
Bibliography:	Includes bibliographical references (leaves 13-14).