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This is the preliminary program for the 2009 Joint Statistical
Meetings in Washington, DC.
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The views expressed here are those of the individual authors and not necessarily those of the ASA or its board, officers, or staff. Back to main JSM 2009 Program page |
= Applied Session,
= Theme Session,
= Presenter| CE_09C | Sun, 8/2/09, 8:30 AM - 5:00 PM | RH-Renaissance Ballroom West B |
| Semiparametric Regression - Continuing Education - Course | ||
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ASA |
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| Instructor(s): David Ruppert, Cornell University, Ciprian M. Crainiceanu, Johns Hopkins University, Raymond J. Carroll, Texas A&M University | ||
| Parametric regression involves fitting a curve to a data set within the confines of parametric families. For example, lines, parabolas, and exponentials. Nonparametric regression, often called smoothing, only imposes the condition that the curve be smooth; the shape of the curve depends primarily on the data. Semiparametric regression combines nonparametric and parametric models. For example, in the analysis of time series data on mortality and air pollution, it is common for the air pollution effects to be modeled linearly, and confounders such as time, temperature, and humidity to be nonparametric. There are many methods for nonparametric estimation including kernel regression, local polynomial regression, smoothing splines, regression splines, and wavelets. However, the method of penalized splines has a number of convenient features, including ease of use; modularity, meaning that each component of the model can be developed separately and in a simple fashion; close connections with parametric statistics so maximum likelihood estimation and likelihood ratio tests can be used; and the ability to be implemented with standard software (e.g., R and WinBUGS). This course will focus on semiparametric models based on penalized splines viewed as linear mixed models, or-for binary, Poisson, and other non-Gaussian outcomes-as generalized linear mixed models. | ||
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JSM 2009
For information, contact jsm@amstat.org
or phone (888) 231-3473. If you have questions about the Continuing Education program,
please contact the Education Department. |