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Nonparametric Kernel Density Estimation and Its Computational Aspects (Paperback, Softcover reprint of the original 1st ed. 2018)
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Nonparametric Kernel Density Estimation and Its Computational Aspects (Paperback, Softcover reprint of the original 1st ed. 2018)
Series: Studies in Big Data, 37
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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.
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