Bandwidth selection for a data sharpening estimator in nonparametric regression

Kanta Naito and Masahiro Yoshizaki

Journal of Multivariate Analysis, 2009, vol. 100, issue 7, 1465-1486

Abstract: 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.

Keywords: Bandwidth; Bias; reduction; Data; sharpening; Kernel; Nonparametric; regression; Plug-in; method (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (1)

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