Smoothing Splines: Methods and Applications
Monographs on Statistics & Applied Probability
by Yuedong Wang
Chapman & Hall/CRC. ISBN 1420077554, 2011.
![[Image of Cover]](bookcover.jpg)
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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.
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Yuedong Wang