Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/92032
Title: Bandwidth selection for kernel density estimation: a Hermite series-based direct plug-in approach
Authors: Tenreiro, Carlos 
Issue Date: 2020
Publisher: Taylor and Francis
Serial title, monograph or event: Journal of Statistical Computation and Simulation
Volume: 90
Issue: 18
Abstract: In this paper we propose a new class of Hermite series-based direct plug-in bandwidth selectors for kernel density estimation and we describe their asymptotic and finite sample behaviours. Unlike the direct plug-in bandwidth selectors considered in the literature, the proposed methodology does not involve multistage strategies and reference distributions are no longer needed. The new bandwidth selectors show a good finite sample performance when the underlying probability density function presents not only "easy-to-estimate" but also "hard-to-estimate" distribution features. This quality, that is not shared by other widely used bandwidth selectors as the classical plug-in or the least-square cross-validation methods, is the most significant aspect of the Hermite series-based direct plug-in approach to bandwidth selection.
URI: http://hdl.handle.net/10316/92032
ISSN: 0094-9655
1563-5163
DOI: 10.1080/00949655.2020.1804571
Rights: embargoedAccess
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais

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