Kernel estimation in high-energy physics

Kyle Cranmer

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)198-207
Number of pages10
JournalComputer Physics Communications
Volume136
Issue number3
DOIs
StatePublished - May 15 2001
Externally publishedYes

Keywords

  • HEPUKeys
  • KEYS
  • Kernel estimation
  • Multivariate probability density estimation
  • Non-parametric
  • PDE
  • RootPDE
  • Unbinned
  • WinPDE

ASJC Scopus subject areas

  • Hardware and Architecture
  • Physics and Astronomy(all)

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