Graybill 2011

Conference Overview

Well-developed nonparametric methods are an essential part of modern data analysis, and an important and growing research topic within statistics. The focus of the conference is on nonparametric and semiparametric modeling and functional estimation methods. The program consists of a short course, invited plenary talks and a contributed poster session.

The conference will bring together some of the top researchers in this area, and the topics of the presentations will range from general overviews of relevant statistical material to more specialized presentations of current developments. The focused yet relaxed nature of the conference will allow for concentrated discussion and interaction among the participants. Following the conference, there will be opportunities for various outdoor activities in the area.

In order to encourage students to participate, we are planning a student poster competition, with the winners receiving travel support awards. The short course on semiparametric regression by Matt Wand will provide a hands-on introduction to the topics in the conference and is particularly suitable for students and other people interested in learning more about smoothing methods.

The conference is co-sponsored by the Department of Statistics at Colorado State University and the ASA Section on Nonparametric Statistics.

If you have questions about the conference, send an email GraybillConference@Stat.ColoState.Edu

We look forward to welcoming you in Fort Collins and Northern Colorado!

Modern Nonparametric Methods - Short Course

SEMIPARAMETRIC REGRESSION

A short-course by Professor Matt Wand
(University of Technology, Sydney, Australia)

June 22, 2011

SHORT-COURSE OVERVIEW

Semiparametric regression is concerned with the flexible 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. The short-course is based on the book `Semiparametric Regression' by D. Ruppert, M.P. Wand and R.J. Carroll (Cambridge University Press, 2003) and a 2009 Electronic Journal of Statistics paper by the same authors which describes more recent developments on the topic.

Registration: 8:00am-9:30am (Pre-Convention)
Course: 9:30am - 4:30pm (Salon I)
Lunch 12:50pm - 2:00pm (Atrium)
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Session 1: Spline smoothing and generalised 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
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ABOUT THE PRESENTER:

Matt Wand is a Distinguished Professor in Statistics at the University of Technology, Sydney, Australia. He has held faculty appointments at Harvard University, Rice University, Texas A&M University, University of New South Wales and University of Wollongong. Professor Wand is an elected Fellow of the Australian Academy of Science, American Statistical Association and the Institute of Mathematical Statistics and was awarded the P.A.P. Moran Medal for statistical research. He has served as an associate editor for the Journal of the American Statistical Association, Biometrika and Statistica Sinica.

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