Journal
COMPUTER PHYSICS COMMUNICATIONS
Volume 136, Issue 3, Pages 198-207Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/S0010-4655(00)00243-5
Keywords
kernel estimation; multivariate probability density estimation KEYS; RootPDE; WinPDE; PDE; HEPUKeys; unbinned; non-parametric
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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.
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