Abstract
This chapter describes the kernel density estimation technique that can be considered a smoothed version of the Parzen windows presented in the Chapter 2. 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 the chapter is devoted to an introduction of two important KDE extensions, namely adaptive KDE and KDE with boundary correction. The notion of kernal derivative estimation (KDDE) is also presented. The final part of the chapter describes how KDE can be used for nonparametric estimation of cumulative distribution function (CDF). The chapter ends with some notes on computational aspects related to KDE.
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Gramacki, A. (2018). Kernel Density Estimation. In: Nonparametric Kernel Density Estimation and Its Computational Aspects. Studies in Big Data, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-71688-6_3
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DOI: https://doi.org/10.1007/978-3-319-71688-6_3
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71687-9
Online ISBN: 978-3-319-71688-6
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