Nonparametric Kernel Density Estimation and Its Computational Aspects

✍ Scribed by Artur Gramacki (auth.)

• Publisher: Springer International Publishing
• Year: 2018
• Tongue: English
• Leaves: 197
• Series: Studies in Big Data 37
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xxix
Introduction (Artur Gramacki)....Pages 1-6
Nonparametric Density Estimation (Artur Gramacki)....Pages 7-24
Kernel Density Estimation (Artur Gramacki)....Pages 25-62
Bandwidth Selectors for Kernel Density Estimation (Artur Gramacki)....Pages 63-83
FFT-Based Algorithms for Kernel Density Estimation and Bandwidth Selection (Artur Gramacki)....Pages 85-118
FPGA-Based Implementation of a Bandwidth Selection Algorithm (Artur Gramacki)....Pages 119-131
Selected Applications Related to Kernel Density Estimation (Artur Gramacki)....Pages 133-158
Conclusion and Further Research Directions (Artur Gramacki)....Pages 159-161
Back Matter ....Pages 163-176

✦ Subjects

Computational Intelligence