Titlebook: Nonparametric Kernel Density Estimation and Its Computational Aspects; Artur Gramacki Book 2018 Springer International Publishing AG 2018

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Introduction, capture only the important patterns, while filtering noise and ignoring the data structures that are deemed not relevant. The functions commonly referred to as filters can serve as examples of typical smoothers. In our treatment of the topic, we focus on one of the most well-known nonparametric smo

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Nonparametric Density Estimation,. technique is briefly presented together with a description of its main drawbacks. To avoid the highlighted problems, at least to some extent, one might use a smart histogram modification known in the literature as an . (ASH). A simple example presented in this chapter shows its advantages over the

paradigm

Kernel Density Estimation,r .. First, the most popular kernel types are presented together with a number of basic definitions both for uni- and multivariate cases and then a review of performance criteria is provided, starting with the univariate case and then extended to the general multivariate case. The subsequent part of

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Bandwidth Selectors for Kernel Density Estimation, and moves on to an overview of the three major types of selectors (that is: . (ROT), . (CV) and . (PI) selectors). The next part of the chapter is devoted to describing these selectors in more detail, both for the uni- and multivariate cases. Finally, a few numerical examples are given. The chapter

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,FPGA-Based Implementation of?a?Bandwidth Selection Algorithm,ithm. In contrast to the results presented in Chapter 5, this chapter describes a hardware-based method, which relies on utilizing the so-called . (FPGA). FPGA devices are not often used for purposes of implementing purely numerical algorithms. The proposed implementation can be seen as a preliminar

Yourself

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Bandwidth Selectors for Kernel Density Estimation,voted to describing these selectors in more detail, both for the uni- and multivariate cases. Finally, a few numerical examples are given. The chapter is rounded off with a short section on the computational issues related to bandwidth selectors.

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2197-6503 its applications.Describes in detail computational-like pro.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 computation

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Kernel Density Estimation, the chapter is devoted to an introduction of two important KDE extensions, namely . KDE and KDE with .. The notion of . (KDDE) is also presented. The final part of the chapter describes how KDE can be used for nonparametric estimation of . (CDF). The chapter ends with some notes on computational aspects related to KDE.