Semiparametric regression during 2003-2007

Ruppert, D; Wand, M; Carroll, RJ

Semiparametric regression during 2003-2007

Ruppert, D Wand, M Carroll, RJ

Publisher:: Institute of Mathematical Statistics
Publication Type:: Journal Article
Citation:: Electronic Journal of Statistics, 2009, 3 (1), pp. 1193 - 1256
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Ruppert, D	en_US
dc.contributor.author	Wand, M	en_US
dc.contributor.author	Carroll, RJ	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	Electronic Journal of Statistics, 2009, 3 (1), 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.	en_US
dc.publisher	Institute of Mathematical Statistics	en_US
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	1	en_US
utslib.citation.volume	3	en_US
utslib.for	0104 Statistics	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.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.notes	Ok-SAM	en_US
pubs.volume	3	en_US

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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.

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