Nonparametric Kernel Density Estimation and Its Computational Aspects
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Beschreibung
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.
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.
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 Includes supplementary material: sn.pub/extras
Autor*in
Artur Gramacki
Themen in »Nonparametric Kernel Density Estimation and Its Computational Aspects«
Nonparametric Statistics Nonparametric Estimators Data Smoothing Kernel Density Estimation KDE Bandwidth Selection Fast Fourier Transform Data Binning Field-programmable Gate Arrays