Semiparametric regression during 2003–2007

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

Research output: Contribution to journalArticlepeer-review

148 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)1193-1256
Number of pages64
JournalElectronic Journal of Statistics
StatePublished - 2009
Externally publishedYes

Bibliographical note

KAUST 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
and Technology.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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