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Nonparametric Kernel Density Estimation and Its Computational Aspects

  • Artur Gramacki
Book

Part of the Studies in Big Data book series (SBD, volume 37)

Table of contents

  1. Front Matter
    Pages i-xxix
  2. Artur Gramacki
    Pages 1-6
  3. Artur Gramacki
    Pages 7-24
  4. Artur Gramacki
    Pages 25-62
  5. Artur Gramacki
    Pages 159-161
  6. Back Matter
    Pages 163-176

About this book

Introduction

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.

Keywords

Nonparametric Statistics Nonparametric Estimators Data Smoothing Kernel Density Estimation KDE Bandwidth Selection Fast Fourier Transform Data Binning Field-programmable Gate Arrays

Authors and affiliations

  • Artur Gramacki
    • 1
  1. 1.Institute of Control and Computation EngineeringUniversity of Zielona Góra Zielona GóraPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-71688-6
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-71687-9
  • Online ISBN 978-3-319-71688-6
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site
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