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Abstract:
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Semiparametric regression methods build on parametric regression models by allowing more flexible relationships between the predictors and response variables. Examples of semiparametric regression include generalized additive models, additive mixed models, and spatial smoothing. Our goal is to provide an easy-to-follow applied course on semiparametric regression methods using R. There is a vast literature on the semiparametric regression methods. However, most of it is geared toward researchers with advanced knowledge of statistical methods. This course is intended for applied statistical analysts who have some familiarity with R. 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 rstan, with most of the course spent doing computing exercises. Attendees are encouraged to bring their laptops. This short course is based on the upcoming book Semiparametric Regression with R by D. Ruppert, M.P. Wand. and J. Harezlak.
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