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Semiparametric regression during 2003-2007
David Ruppert1, M P Wand, Raymond J Carroll
1School of Operations Research and Information Engineering, Cornell University, 1170 Comstock Hall, Ithaca, NY 14853, U.S.A.
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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