Smoothing Splines Methods And Applications | Statistics

A general class of powerful and flexible modeling techniques spline smoothing has attracted a great deal of research attention in recent years and has been widely used in many application areas from medicine to economics. Smoothing Splines: Methods and Applications covers basic smoothing spline models including polynomial periodic spherical thin-plate L- and partial splines as well as more advanced models such as smoothing spline ANOVA extended and generalized smoothing spline ANOVA vector spline nonparametric nonlinear regression semiparametric regression and semiparametric mixed-effects models. It also presents methods for model selection and inference. The book provides unified frameworks for estimation inference and software implementation by using the general forms of nonparametric/semiparametric linear/nonlinear and fixed/mixed smoothing spline models. The theory of reproducing kernel Hilbert space (RKHS) is used to present various smoothing spline models in a unified fashion. Although this approach can be technical and difficult the author makes the advanced smoothing spline methodology based on RKHS accessible to practitioners and students. He offers a gentle introduction to RKHS keeps theory at a minimum level and explains how RKHS can be used to construct spline models. Smoothing Splines offers a balanced mix of methodology computation implementation software and applications. It uses R to perform all data analyses and includes a host of real data examples from astronomy economics medicine and meteorology. The codes for all examples along with related developments can be found on the book’s web page. |Smoothing Splines Methods and Applications | Statistics