Electronic Journal of Statistics

Semiparametric regression during 2003–2007

David Ruppert, M.P. Wand, and Raymond J. Carroll

Full-text: Open access

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.

Article information

Source
Electron. J. Statist. Volume 3 (2009), 1193-1256.

Dates
First available: 4 December 2009

Permanent link to this document
http://projecteuclid.org/euclid.ejs/1259944245

Digital Object Identifier
doi:10.1214/09-EJS525

Mathematical Reviews number (MathSciNet)
MR2566186

Zentralblatt MATH identifier
06166479

Subjects
Primary: 60-02: Research exposition (monographs, survey articles) 60G05: Foundations of stochastic processes 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

Citation

Ruppert, David; Wand, M.P.; Carroll, Raymond J. Semiparametric regression during 2003–2007. Electronic Journal of Statistics 3 (2009), 1193--1256. doi:10.1214/09-EJS525. http://projecteuclid.org/euclid.ejs/1259944245.


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