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.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: Supported by grants from the National Cancer Institute (CA57030) and the National
Science Foundation (DMS-0805975).
Supported by a grant from the Australian Research Council (DP0877055).
Supported by grants from the National Cancer Institute (CA57030, CA104620), and also
in part by award number KUS-CI-016-04 made by the King Abdullah University of Science
This publication acknowledges KAUST support, but has no KAUST affiliated authors.