Penalized splines and reproducing kernel methods

Pearce, ND; Wand, MP

Penalized splines and reproducing kernel methods

Pearce, ND Wand, MP

Permalink

Publication Type:: Journal Article
Citation:: American Statistician, 2006, 60 (3), pp. 233 - 240
Issue Date:: 2006-08-01

Closed Access

	Filename	Description	Size
	2010000457OK.pdf		382.46 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Pearce, ND	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2006-08-01	en_US
dc.identifier.citation	American Statistician, 2006, 60 (3), pp. 233 - 240	en_US
dc.identifier.issn	0003-1305	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14492
dc.description.abstract	Two data analytic research areas - penalized splines and reproducing kernel methods - have become very vibrant since the mid-1990s. This article shows how the former can be embedded in the latter via theory for reproducing kernel Hilbert spaces. This connection facilitates cross-fertilization between the two bodies of research. In particular, connections between support vector machines and penalized splines are established. These allow for significant reductions in computational complexity, and easier incorporation of special structure such as additivity. © 2006 American Statistical Association.	en_US
dc.relation.ispartof	American Statistician	en_US
dc.relation.isbasedon	10.1198/000313006X124541	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Penalized splines and reproducing kernel methods	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	60	en_US
utslib.for	0104 Statistics	en_US
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	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	60	en_US

Abstract:

Two data analytic research areas - penalized splines and reproducing kernel methods - have become very vibrant since the mid-1990s. This article shows how the former can be embedded in the latter via theory for reproducing kernel Hilbert spaces. This connection facilitates cross-fertilization between the two bodies of research. In particular, connections between support vector machines and penalized splines are established. These allow for significant reductions in computational complexity, and easier incorporation of special structure such as additivity. © 2006 American Statistical Association.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/14492