Kernel estimation in high-energy physics

Kyle Cranmer

    Research output: Contribution to journalArticlepeer-review

    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

    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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