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Nonparametric Kernel Density Estimation and Its Computational Aspects

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Nonparametric Kernel Density Estimation and Its Computational Aspects/ by Artur Gramacki.
Author:	Gramacki, Artur.
Description:	XXIX, 176 p. 70 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-319-71688-6
ISBN:	9783319716886

Nonparametric Kernel Density Estimation and Its Computational Aspects
Gramacki, Artur.

Nonparametric Kernel Density Estimation and Its Computational Aspects[electronic resource] /by Artur Gramacki. - 1st ed. 2018. - XXIX, 176 p. 70 illus.online resource. - Studies in Big Data,372197-6503 ;. - Studies in Big Data,8.

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.

ISBN: 9783319716886

Standard No.: 10.1007/978-3-319-71688-6doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Nonparametric Kernel Density Estimation and Its Computational Aspects
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