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Bandwidth selection for kernel density estimation: a Hermite series-based direct plug-in approach
(Journal Article)
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<Specific Information>
<Reference List>
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Type:
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Journal Article
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National /International:
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International
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| Title: |
Bandwidth selection for kernel density estimation: a Hermite series-based direct plug-in approach
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Publication Date:
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2020
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Authors:
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- Carlos Tenreiro
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Journal Name:
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Journal of Statistical Computation and Simulation
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Volume:
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90
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Number:
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18
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Pages:
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3433-3453
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Abstract:
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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.
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© 2012 Centre for Mathematics, University of Coimbra, funded by
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