Cambridge University Press published in 2003,  This is a  classic textbook in this area with examples and theories. Here is part of it's introduction.

The primary aim of this book is to guide researchers needing to flexibly incorporate
nonlinear relationships into their regression analyses. Flexible nonlinear
regression is traditionally known as nonparametric regression; it differs from
parametric regression in that the shape of the functional relationships are not predetermined
but can adjust to capture unusual or unexpected features of the data.
Almost all existing regression texts treat either parametric or nonparametric
regression exclusively. The level of exposition between books of either type differs
quite alarmingly. In this book we argue that nonparametric regression can
be viewed as a relatively simple extension of parametric regression and treat the
two together. We refer to this combination as semiparametric regression. Our
approach to semiparametric regression is based on penalized regression splines
and mixed models. Indeed, every model in this book is a special case of the linear
mixed model or its generalized counterpart. This makes the methodology
modular and is in keeping with our general philosophy of minimalist statistics
(see Section 19.2), where the amount of methodology, terminology, and so on is
kept to a minimum. This is the first smoothing book that makes use of the mixed
model representation of smoothers.