Semiparametric regression during 2003–2007*

Ruppert, D; Wand, MP; Carroll, RJ

Semiparametric regression during 2003–2007*

Ruppert, D Wand, MP

Carroll, RJ

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Publication Type:: Journal Article
Citation:: Electronic Journal of Statistics, 2009, 3 pp. 1193 - 1256
Issue Date:: 2009-01-01

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Field	Value	Language
dc.contributor.author	Ruppert, D	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.contributor.author	Carroll, RJ	en_US
dc.date.issued	2009-01-01	en_US
dc.identifier.citation	Electronic Journal of Statistics, 2009, 3 pp. 1193 - 1256	en_US
dc.identifier.issn	1935-7524	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14511
dc.description.abstract	Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application. © 2009, Institute of Mathematical Statistics. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0877055
dc.relation.ispartof	Electronic Journal of Statistics	en_US
dc.relation.isbasedon	10.1214/09-EJS525	en_US
dc.title	Semiparametric regression during 2003–2007*	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.for	0104 Statistics	en_US
dc.location.activity	Kuala Lumpur, Malaysia
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	open_access
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application. © 2009, Institute of Mathematical Statistics. All rights reserved.

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

http://hdl.handle.net/10453/14511