Nonparametric Kernel Density Estimation and Its Computational Aspects

Name: Nonparametric Kernel Density Estimation and Its Computational Aspects
Brand: Springer
SKU: 9783319716879
Price: 10174.0 INR
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by Artur Gramacki

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ISBN13: 9783319716879
Binding: Hardcover
Subject: N/A
Publisher: Springer
Publisher Imprint: Springer
Publication Date: January 22, 2018
Pages: 176
Original Price: EUR 139.99
Language: English
Edition: 2018
Item Weight: 468 grams
BISAC Subject(s): Artificial Intelligence / General, Database Administration & Management, and Data Science / General

About The Book

From the Back Cover

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 Author

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.

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