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.
Ruppert, D., Wand, M. P.. & Carroll, R. J. (2009). Semiparametric regression during 2003-2007. Electronic Journal of Statistics, 3 (N/A), 1193-1256.