Nonparametric Kernel density estimation and its computational aspects
著者
書誌事項
Nonparametric Kernel density estimation and its computational aspects
（Studies in big data, v. 37）
Springer, c2018
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注記
Includes bibliographical references (p. 163-171) and index
内容説明・目次
内容説明
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.
目次
Introduction.- Nonparametric density estimation.- Kernel density estimation .- Bandwidth selectors for kernel density estimation.- FFT-based algorithms for kernel density estimation and band-width selection.- FPGA-based implementation of a bandwidth selection algorithm.- Selected applications related to kernel density estimation.- Conclusion and further research.
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