Nonparametric Kernel Density Estimation and Its Computational Aspects

This book describes computational problems related to kernel density estimation (KDE)—one of the most important and widely used data smoothing techniques. It also includes detailed descriptions of novel FFT-based algorithms for both KDE computations and bandwidth selection.

 The theory of KDE appears to have matured and is now well developed and understood. However, comparatively little progress has been 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. It includes both background information and much more sophisticated material; hence it will also be of interest to experienced KDE researchers.

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

• ISBN: 978-3-319-71687-9
• Editorial: Springer
• Encuadernacion: Cartoné
• Fecha Publicación: 18/03/2018
• Nº Volúmenes: 1
• Idioma: Inglés