Accuracy of binned kernel functional approximations

González-Manteiga, W; Sánchez-Sellero, C; Wand, MP

Accuracy of binned kernel functional approximations

González-Manteiga, W Sánchez-Sellero, C Wand, MP

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Publication Type:: Journal Article
Citation:: Computational Statistics and Data Analysis, 1996, 22 (1), pp. 1 - 16
Issue Date:: 1996-06-14

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Field	Value	Language
dc.contributor.author	González-Manteiga, W	en_US
dc.contributor.author	Sánchez-Sellero, C	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	1996-06-14	en_US
dc.identifier.citation	Computational Statistics and Data Analysis, 1996, 22 (1), pp. 1 - 16	en_US
dc.identifier.issn	0167-9473	en_US
dc.identifier.uri	http://hdl.handle.net/10453/21949
dc.description.abstract	Virtually all common bandwidth selection algorithms are based on a certain type of kernel functional estimator. Such estimators can be computationally very expensive, so in practice they are often replaced by fast binned approximations. This is especially worthwhile when the bandwidth selection method involves iteration. Results for the accuracy of these approximations are derived and then used to provide an understanding of the number of binning grid points required to achieve a given level of accuracy. Our results apply to both univariate and multivariate settings. Multivariate contexts are of particular interest since the cost due to having a higher number of grid points can be quite significant.	en_US
dc.relation.ispartof	Computational Statistics and Data Analysis	en_US
dc.relation.isbasedon	10.1016/0167-9473(96)88030-3	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Accuracy of binned kernel functional approximations	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	22	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	1403 Econometrics	en_US
dc.location.activity	The Chinese University of Hong Kong
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	22	en_US

Abstract:

Virtually all common bandwidth selection algorithms are based on a certain type of kernel functional estimator. Such estimators can be computationally very expensive, so in practice they are often replaced by fast binned approximations. This is especially worthwhile when the bandwidth selection method involves iteration. Results for the accuracy of these approximations are derived and then used to provide an understanding of the number of binning grid points required to achieve a given level of accuracy. Our results apply to both univariate and multivariate settings. Multivariate contexts are of particular interest since the cost due to having a higher number of grid points can be quite significant.

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http://hdl.handle.net/10453/21949