This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent
advances regarding confidence bands and geometric/topological features. We begin with a …

There has been major progress in recent years in data-based bandwidth selection for kernel
density estimation. Some “second generation” methods, including plug-in and smoothed …

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 …

Clarifies modern data analysis through nonparametric density estimation for a complete
working knowledge of the theory and methods Featuring a thoroughly revised presentation …

Nonparametric density gradient estimation using a generalized kernel approach is
investigated. Conditions on the kernel functions are derived to guarantee asymptotic …

Let f be a probability density on an interval I, finite or infinite: I includes its finite endpoints, if
any; and f vanishes outside of I. Let X1,..., X k be independent random variables, with …

The existence of high speed, inexpensive computing has made it easy to look at data in
ways that were once impossible. Where once a data analyst was forced to make restrictive …

Not so long ago, multivariate analysis consisted solely of linear methods illustrated on small
to medium-sized data sets. Moreover, statistical computing meant primarily batch processing …

This book focuses on tools and techniques for building regression models using real-world
data and assessing their validity. A key theme throughout the book is that it makes sense to …