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.
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