Nonparametric Density Estimation
Nonparametric Density Estimation
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DOI：
10.1002/9781118555552.ch10
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发表时间：
2013-03
期刊：
影响因子：
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通讯作者：
G. Givens;J. Hoeting
中科院分区：
文献类型：
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作者：
G. Givens;J. Hoeting
本章介绍与非参数密度估计相关的背景材料。直方图（及其扩展，称为 ASH，请参见第 2.3 节）、Parzen 窗和 k 最近邻等技术是非参数密度估计应用的核心。因此，为了完整起见，我们决定用一章来描述这些内容，并让经验不足的读者在非参数估计方面发展他们的直觉。大多数材料仅考虑单变量情况；然而，将结果扩展到涵盖多个变量通常是一项简单的任务。本章的组织如下： 节。 2.2 简要概述了与直方图相关的基本概念。第2.3节致力于描述某些众所周知的直方图的智能扩展，旨在避免它们的一些缺点。第 2.4 节介绍了与非参数密度估计相关的基本概念。第 2.5 节专门讨论 Parzen 窗户，而第 2.5 节则专门讨论 Parzen 窗户。 2.6 k近邻方法。
This chapter describes the background material related to the nonparametric density estimation. Techniques such as histograms (together with its extension, known as ASH, see Sect. 2.3), Parzen windows and k-nearest neighbors are at the core of the applications of nonparametric density estimation. For that reason, we decided to include a chapter describing these for the sake of completeness and to allow less experienced readers develop their intuitions in terms of the nonparametric estimation. Most of the material is presented taking into account only the univariate case; extending the results to cover more than one variable, however, is often a straightforward task. The chapter is organized as follows: Sect. 2.2 presents a short overview of the fundamental concepts related to histograms. Section2.3 is devoted to a description of a smart extension of certain well-known histograms aimed at avoiding some of their drawbacks. Section2.4 presents basic concepts related to the nonparametric density estimation. Section2.5 is devoted to the Parzen windows, while Sect. 2.6 to the k-nearest neighbors approach.