JSM 2011 Online Program

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Activity Details

207355 TBD
Smoothing Splines: Methods and Applications — Continuing Education Course
Instructor(s): Yuedong Wang, University of California - Santa Barbara
This short course is about a particular class of modern nonparametric regression methods called spline smoothing for estimating functions of one and several variables. Special models such as polynomial, periodic, thin plate, partial and tensor product smoothing splines for Gaussian data will be covered. The general form of smoothing spline models using reproducing kernel Hilbert space (RKHS) will also be discussed, however, no prior knowledge of these spaces is assumed. Data based methods for estimating the optimal amount of smoothing such as cross validation, generalized cross validation, generalized maximum likelihood will be explored. The short course will focuses on methodology and application. We will provides a gentle introduction to the RKHS, keeps theory at the minimum level, and provides details on how the RKHS can be used to construct spline models. Much of the exposition is based on the analysis of real examples using R. Prerequisite: two years of graduate level statistics courses such as statistical inference, generalized linear models, data analysis and regression. The short course will use partial material covered by the book: Y. Wang (2011), Smoothing Splines: Methods and Applications, Chapman & Hall.

2011 JSM Online Program Home

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