Open Access
2009 Semiparametric regression during 2003–2007
David Ruppert, M.P. Wand, Raymond J. Carroll
Electron. J. Statist. 3: 1193-1256 (2009). DOI: 10.1214/09-EJS525


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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David Ruppert. M.P. Wand. Raymond J. Carroll. "Semiparametric regression during 2003–2007." Electron. J. Statist. 3 1193 - 1256, 2009.


Published: 2009
First available in Project Euclid: 4 December 2009

zbMATH: 1326.62094
MathSciNet: MR2566186
Digital Object Identifier: 10.1214/09-EJS525

Primary: 60-02 , 60G05 , 60G08

Keywords: asymptotics , boosting , BUGS , Functional data analysis , generalized linear mixed models , graphical models , hierarchical Bayesian models , Kernel machines , longitudinal data analysis , mixed models , Monte Carlo methods , penalized splines , spatial statistics

Rights: Copyright © 2009 The Institute of Mathematical Statistics and the Bernoulli Society

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