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JSM 2012 Online Program

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Activity Details

CE_22C Tue, 7/31/2012, 8:30 AM - 5:00 PM HQ-Indigo 206
Generalized Additive Models and Their Extensions: The Penalized Regression Spline Approach — Continuing Education Course
ASA , Section on Physical and Engineering Sciences
Instructor(s): Simon Wood, University of Bath
This course provides an overview of the theory of generalized additive models represented by reduced rank penalized splines, and their practical use with the mgcv package in R. Here generalized additive models include generalized additive mixed models, varying coefficient/geographic regression models, structured additive regression models, generalized linear additive smooth structure models, signal regression models etc, since all of these fit into the same inferential and computational framework (quadratically penalized GLMs). The course will give a compact overview of the essential theory of penalized regression splines and GAMs, focusing on the key theoretical concepts that underpin the more detailed literature: bases, penalties, the Bayesian model of smoothing, and smoothing parameter selection. It will then cover the various types of smooth (one dimensional, isotropic and tensor product interactions) that form the basic toolkit for model construction. Model checking, building and selection will be discussed, including practical exercises with the mgcv package in R. The course will finish with a look at some more advanced GAM topics: spatial and temporal auto-correlation, functional data analysis, and inference via posterior simulation. Participants should preferably bring a laptop, with the latest version of R installed. Reading: Wood SN, (2006) Generalized Additive Models: An introduction with R

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