Advances in computation and the fast and cheap computational facilities now available to
statisticians have had a significant impact upon statistical research, and especially the …

We review different approaches to nonparametric density and regression estimation. Kernel
estimators are motivated from local averaging and solving ill-posed problems. Kernel …

Savitzky–Golay (SG) filtering, based on local least-squares fitting of the data by polynomials,
is a popular method for smoothing data and calculations of derivatives of noisy data. At …

Tree boosting has empirically proven to be a highly effective approach to predictive
modeling. It has shown remarkable results for a vast array of problems. For many years …

Data-analytic approaches to regression problems, arising from many scientific disciplines
are described in this book. The aim of these nonparametric methods is to relax assumptions …

Although there has been a surge of interest in density estimation in recent years, much of the
published research has been concerned with purely technical matters with insufficient …

This chapter discusses the innovations of additional flexible methods for modeling an
individual term in an additive model. It focuses on how we fit additive models. A general and …

We give a basic introduction to Gaussian Process regression models. We focus on
understanding the role of the stochastic process and how it is used to define a distribution …

Locally weighted regression, or loess, is a way of estimating a regression surface through a
multivariate smoothing procedure, fitting a function of the independent variables locally and …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …