Studies in Big Data

Nonparametric Kernel Density Estimation and Its Computational Aspects

Authors: Gramacki, Artur

  • Contains both background information and much more sophisticated material on kernel density estimation (KDE), its computational aspects, and its applications
  • Describes in detail computational-like problems related to KDE
  • Includes R source codes for replicating all the figures included in the book—making it a good source for newcomers to the field
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Buy this book

eBook 95,19 €
price for Spain (gross)
  • ISBN 978-3-319-71688-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 119,59 €
price for Spain (gross)
  • ISBN 978-3-319-71687-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

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.

About the authors

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.   

 

Table of contents (8 chapters)

  • Introduction

    Gramacki, Artur

    Pages 1-6

    Preview Buy Chapter 30,19 €
  • Nonparametric Density Estimation

    Gramacki, Artur

    Pages 7-24

    Preview Buy Chapter 30,19 €
  • Kernel Density Estimation

    Gramacki, Artur

    Pages 25-62

    Preview Buy Chapter 30,19 €
  • Bandwidth Selectors for Kernel Density Estimation

    Gramacki, Artur

    Pages 63-83

    Preview Buy Chapter 30,19 €
  • FFT-Based Algorithms for Kernel Density Estimation and Bandwidth Selection

    Gramacki, Artur

    Pages 85-118

    Preview Buy Chapter 30,19 €

Buy this book

eBook 95,19 €
price for Spain (gross)
  • ISBN 978-3-319-71688-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 119,59 €
price for Spain (gross)
  • ISBN 978-3-319-71687-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Nonparametric Kernel Density Estimation and Its Computational Aspects
Authors
Series Title
Studies in Big Data
Series Volume
37
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG
eBook ISBN
978-3-319-71688-6
DOI
10.1007/978-3-319-71688-6
Hardcover ISBN
978-3-319-71687-9
Series ISSN
2197-6503
Edition Number
1
Number of Pages
XXIX, 176
Number of Illustrations and Tables
70 b/w illustrations
Topics