Multi-step ahead forecasting is still an open challenge in time series forecasting. Several
approaches that deal with this complex problem have been proposed in the literature but an …

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 …

Time series clustering is an active research area with applications in a wide range of fields.
One key component in cluster analysis is determining a proper dissimilarity measure …

Nonparametric methods play a central role in modern empirical work. While they provide
inference procedures that are more robust to parametric misspecification bias, they may be …

Long-memory, or more generally fractal, processes are known to play an important role in
many scientific disciplines and applied fields such as physics, geophysics, hydrology …

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 …

S is a powerful environment for the statistical and graphical analysis of data. It provides the
tools to implement many statistical ideas that have been made possible by the widespread …

B-splines are attractive for nonparametric modelling, but choosing the optimal number and
positions of knots is a complex task. Equidistant knots can be used, but their small 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 …

The book describes the use of smoothing techniques in statistics, including both density
estimation and nonparametric regression. Considerable advances in research in this area …