Oberwolfach Seminar: Semiparametric and Nonparametric Regression

Date

October 18th - October 24th, 2009

Organizers

Raymond Carroll, College Station

Ciprian Crainiceanu, Baltimore

Matthew Wand, Wollongong

Programme

Semiparametric regression is concerned with the flexible nearly nonparametric incorporation of nonlinear functional relationships in regression analyses. Assuming only a basic familiarity with ordinary regression, this short-course explains the techniques and benefits of semiparametric regression in a concise and modular fashion. Spline functions, linear mixed models and Bayesian hierarchical models are shown to play an important role in semiparametric regression. There will be a strong emphasis on implementation in R and BUGS.

Besides learning about modern semiparametric and nonparametric regression, students in the course will be exposed to the linear mixed model in a unique way.

Topics Covered

• Session 1: Spline smoothing and generalized additive models
• Session 2: Linear mixed model approach
• Session 3: Bayesian hierarchical model approach
• Session 4: Additive mixed models
• Session 5: Bivariate smoothing
• Session 6: Non-standard semiparametric regression

Software SemiPar

Literature

Suggested Text
Semiparametric Regression by David Ruppert, Matthew P. Wand and Raymond J. Carroll (Cambridge University Press, 2003).

This book is available in paperback. See the book web site and a book review by Michael R. Chernick

See also
Crainiceanu, C., Ruppert, D. and Wand, M.P.:
Bayesian Analysis for Penalized Spline Regression Using WinBUGS.
Volume 14, 2005, Issue 14 of Journal of Statistical Software.

Deadline for applications

September 1st, 2009

The seminars take place at the Mathematisches Forschungsinstitut Oberwolfach. The number of participants is restricted to 24. The Institute covers accommodation and food. Travel expenses cannot be reimbursed. Applications including

• full name and address, including e-mail address
• present position, university
• name of supervisor of Ph.D. thesis
• a short summary of previous work and interest

should be sent as hard copy or by e-mail (.ps or .pdf file) to:

    Prof. Dr. Gert-Martin Greuel
    Universität Kaiserslautern
    Fachbereich Mathematik
    Erwin Schrödingerstr.
    67663 Kaiserslautern, Germany