Higher order bias reduction of kernel density and density derivative estimation at boundary points

A new, direct method is developed for reducing, to an arbitrary order, the boundary bias of kernel density and density derivative estimators. The basic asymptotic properties of the estimators are derived. Simple examples are provided. A number of simulations are reported, which demonstrate the viability and efficacy of the approach compared to several popular alternatives.

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Year of publication:	2009
Authors:	Bearse, Peter ; Rilstone, Paul
Published in:	Nonparametric econometric methods. - Bingley, U.K : Emerald, ISBN 978-1-84950-624-3. - 2009, p. 319-331
Subject:	Schätztheorie \| Estimation theory \| Statistische Verteilung \| Statistical distribution \| Derivat \| Derivative \| Systematischer Fehler \| Bias \| Nichtparametrische Schätzung \| Nonparametric estimation

Type of publication:	Article
Language:	English
Other identifiers:	10.1108/S0731-9053(2009)0000025013 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10015382580