Bandwidth selection for a data sharpening estimator in nonparametric regression

This paper is concerned with data-based selection of the bandwidth for a data sharpening estimator in nonparametric regression. Two kinds of bandwidths are considered: a bandwidth vector which has a different bandwidth for each covariate, and a scalar bandwidth that is common for all covariates. A p...

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Veröffentlicht in:	Journal of multivariate analysis 2009-08, Vol.100 (7), p.1465-1486
Hauptverfasser:	Naito, Kanta, Yoshizaki, Masahiro
Format:	Artikel
Sprache:	eng
Schlagworte:	Bandwidth Bandwidth Bias reduction Data sharpening Kernel Nonparametric regression Plug-in method Bandwidths Bias reduction Combinatorics Combinatorics. Ordered structures Data sharpening Estimating techniques Exact sciences and technology Graph theory Kernel Mathematics Multivariate analysis Nonparametric inference Nonparametric regression Parametric inference Plug-in method Probability and statistics Regression analysis Sciences and techniques of general use Simulation Statistics Studies
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Beschreibung
Zusammenfassung:	This paper is concerned with data-based selection of the bandwidth for a data sharpening estimator in nonparametric regression. Two kinds of bandwidths are considered: a bandwidth vector which has a different bandwidth for each covariate, and a scalar bandwidth that is common for all covariates. A plug-in method is developed and its theoretical performance is fully investigated. The proposed plug-in method works efficiently in our simulation study.
ISSN:	0047-259X 1095-7243
DOI:	10.1016/j.jmva.2008.12.016