Kernel estimation in high-energy physics
Kernel estimation in high-energy physics
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DOI：
10.1016/s0010-4655(00)00243-5
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发表时间：
2001-05-15
影响因子：
6.3
通讯作者：
Cranmer, K
中科院分区：
文献类型：
--
作者：
Cranmer, K
核估计提供概率密度函数的未分组和非参数估计，从该概率密度函数中提取一组数据。在第一部分中，在简单讨论了参数和非参数方法之后，本文介绍了单变量和多变量情形下的核估计理论。第二部分讨论核估计在高能物理中的一些应用。第三部分提供了可用的单变量和多变量包的概述。本文最后讨论了核估计技术的固有优势和系统误差与估计的父分布。(C)2001 Elsevier Science B.V.保留所有权利。
Kernel estimation provides an unbinned and non-parametric estimate of the probability density function from which a set of data is drawn. In the first section, after a brief discussion on parametric and non-parametric methods, the theory of kernel estimation is developed for univariate and multivariate settings. The second section discusses some of the applications of kernel estimation to high-energy physics. The third section provides an overview of the available univariate and multivariate packages. This paper concludes with a discussion of the inherent advantages of kernel estimation techniques and systematic errors associated with the estimation of parent distributions. (C) 2001 Elsevier Science B.V. All rights reserved.