Penalized Spline Regression and Mixed Models - a Promising Alliance
by Goeran Kauermann
Abstract: Penalized spline fitting as smoothing method has achieved recognizable popularity over the last years. Tracing back to Eilers und Marx (Statistical Science, 1996) the book by Ruppert, Wand & Carroll (2003, Cambridge University Press) shows the versatility and flexibility of the approach.
The presentation starts with an introduction to penalized spline smoothing. Basic ideas are illuminated and supplemented by data examples. In particular the link between penalized spline smoothing and linear mixed models will be exhibited. This allows to select the smoothing parameters based on the maximum likelihood principle. The idea is extended with a number of research results including smoothing in the presence of correlated errors, local adaptive smoothing and duration time modeling. Each extension is motivated and supported with a data example.
For More Information: Dr. Guoqi Qian, email@example.com