9th EMR-IBS and Italian Region conference
Thessaloniki, 8-12 May 2017
Jarek Harezlak for "Semiparametric Regression with R"
Semiparametric regression methods build on parametric regression
models by allowing more flexible relationships between the predictors and the
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 towards researchers with advanced knowledge of statistical methods. This course is intended for applied
statisticians 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 hierarchical models are shown to play an important role in semiparametric regression.
There will be a strong emphasis on implementation in R.
Session 1: Penalized Spline Smoothing
Session 2: Linear mixed model approach
Section 3: Generalized additive models
Session 4: Semiparametric Regression for Analysis of Longitudinal Data
Session 5: Bivariate smoothing and spatial models
Session 6: Non-standard semiparametric regression