Bandwidth selection for a data sharpening estimator in nonparametric regression

Autor:	Masahiro Yoshizaki, Kanta Naito
Rok vydání:	2009
Předmět:	Statistics and Probability Numerical Analysis Scalar (mathematics) Bandwidth (signal processing) Estimator Regression analysis Sharpening Nonparametric regression Statistical simulation Statistics Covariate Statistics::Methodology Statistics Probability and Uncertainty Algorithm Mathematics
Zdroj:	Journal of Multivariate Analysis. 100(7):1465-1486
ISSN:	0047-259X
DOI:	10.1016/j.jmva.2008.12.016
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe48e8dc3d2306bf7c01cf215fa8e207 Zobrazit plný text záznamu Full Text from ScienceDirect