Biased crossvalidation for a kernel regression estimator and its derivatives /

Bibliographic Details
Main Authors:	Härdle, Wolfgang (Author), Carroll, Raymond J. (Author)
Corporate Authors:	United States. Air Force. Office of Scientific Research (sponsoring body.), Sonderforschungsbereich 303--"Information und die Koordination wirtschaftlicher Aktivitäten" (sponsoring body.)
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. 68.
Subjects:	Regression analysis. Kernel functions. Estimation theory. Nonparametric statistics. Mathematical statistics. Statistics > Nonparametric inference > Nonparametric regression. Statistics > Nonparametric inference > Density estimation. Bandwidth selection Bias correction Mean squared error

Description
Abstract:	For univariate nonparametric regression, we compute the mean squared error of a kernel regression estimator and its derivatives (Gasser and Müller, 1984), extending slightly the conditions of applicability of this estimator. We show how to estimate this mean squared error and thus the best smoothing parameter by what Scott and Terrell (1987) call biased crossvalidation, which is essentially a refined version of the "plug-in" method. This bandwidth estimator is shown to be asymptotically optimal in the sense of Härdle and Marron (1985).
Item Description:	Funding information taken from leaf i.
Physical Description:	13 leaves ; 28 cm
Bibliography:	Includes bibliographical references (leaf 5).