Nonparametric Kernel Density Estimation and Its Computational Aspects

Softcover Nachdruck of the original 1. Auflage 2018
ISBN: 978-3-319-89094-4
Verlag: Springer International Publishing

Buch, Englisch, Band 37, 176 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 324 g

Reihe: Studies in Big Data

This book describes computational problems related to kernel density estimation (KDE) – one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented.

The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this.

The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting.

The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.

Zielgruppe

Research

Autoren/Hrsg.

Gramacki, Artur

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data

Inhaltsverzeichnis

Introduction.- Nonparametric density estimation.- Kernel density estimation .- Bandwidth selectors for kernel density estimation.- FFT-based algorithms for kernel density estimation and band-width selection.- FPGA-based implementation of a bandwidth selection algorithm.- Selected applications related to kernel density estimation.- Conclusion and further research.

Über Autor(innen)

Artur Gramacki is an assistant professor at the Institute of Control and Computation Engineering of the University of Zielona Góra, Poland. His main interests cover general exploratory data analysis, while recently he has focused on parametric and nonparametric statistics as well as kernel density estimation, especially its computational aspects. In his career, he has also been involved in many projects related to the design and implementation of commercial database systems, mainly using Oracle RDBMS. He is a keen supporter of the R Project for Statistical Computing, which he tries to use both in his research and teaching activities.