Smoothing Splines: Methods and Applications

Smoothing spline models are a powerful and flexible modeling technique used in many application areas,from medicine to economics. Smoothing Splines: Methods and Applications covers basic and advanced models,model selection and inference,and provides unified frameworks for estimation,inference,and software implementation. It uses R to perform data analyses and includes real data examples from astronomy,economics,medicine,and meteorology.

Format: Paperback / softback
Length: 394 pages
Publication date: 21 January 2023
Publisher: Taylor & Francis Ltd

Spline smoothing is a powerful and flexible modeling technique that has garnered significant research attention in recent years and has been widely applied across various application domains, ranging from medicine to economics. This comprehensive book, Smoothing Splines: Methods and Applications, delves into the realm of smoothing spline models, encompassing a wide range of basic and advanced models. It begins by introducing fundamental smoothing spline models such as polynomial, periodic, spherical, thin-plate, L-, and partial splines, along with more sophisticated models like smoothing spline ANOVA, extended and generalized smoothing spline ANOVA, vector spline, nonparametric nonlinear regression, semiparametric regression, and semiparametric mixed-effects models. Furthermore, the book presents methods for model selection and inference, offering unified frameworks for estimation, inference, and software implementation using general forms of nonparametric/semiparametric, linear/nonlinear, and fixed/mixed smoothing spline models.

The theory of reproducing kernel Hilbert space (RKHS) is employed to present various smoothing spline models in a unified manner. While this approach may be technical and challenging, the author endeavors to make advanced smoothing spline methodology based on RKHS accessible to practitioners and students. He provides a gentle introduction to RKHS, maintains a minimum level of theory, and demonstrates how RKHS can be utilized to construct spline models.

Smoothing Splines offers a well-balanced blend of methodology, computation, implementation, software, and applications. It utilizes the R programming language for all data analyses and includes a wealth of real-world data examples from astronomy, economics, medicine, and meteorology. The book's web page provides access to the codes for all examples, along with related developments.

In summary, Smoothing Splines: Methods and Applications is an essential resource for researchers, practitioners, and students interested in developing and applying smoothing spline models. With its comprehensive coverage of basic and advanced models, practical methodologies, and real-world data examples, this book empowers users to harness the power of spline smoothing in their research and practical endeavors.

Weight: 730g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032477626