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

    Fingerprint

    High energy physics
    physics
    probability density functions
    systematic errors
    energy
    Systematic errors
    Probability density function
    estimates

    Keywords

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

    ASJC Scopus subject areas

    • Computer Science Applications
    • Physics and Astronomy(all)

    Cite this

    Kernel estimation in high-energy physics. / Cranmer, Kyle.

    In: Computer Physics Communications, Vol. 136, No. 3, 15.05.2001, p. 198-207.

    Research output: Contribution to journalArticle

    Cranmer, Kyle. / Kernel estimation in high-energy physics. In: Computer Physics Communications. 2001 ; Vol. 136, No. 3. pp. 198-207.
    @article{ff09cbfe5fd64b098bc26e5e2d291b68,
    title = "Kernel estimation in high-energy physics",
    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.",
    keywords = "HEPUKeys, Kernel estimation, KEYS, Multivariate probability density estimation, Non-parametric, PDE, RootPDE, Unbinned, WinPDE",
    author = "Kyle Cranmer",
    year = "2001",
    month = "5",
    day = "15",
    doi = "10.1016/S0010-4655(00)00243-5",
    language = "English (US)",
    volume = "136",
    pages = "198--207",
    journal = "Computer Physics Communications",
    issn = "0010-4655",
    publisher = "Elsevier",
    number = "3",

    }

    TY - JOUR

    T1 - Kernel estimation in high-energy physics

    AU - Cranmer, Kyle

    PY - 2001/5/15

    Y1 - 2001/5/15

    N2 - 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.

    AB - 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.

    KW - HEPUKeys

    KW - Kernel estimation

    KW - KEYS

    KW - Multivariate probability density estimation

    KW - Non-parametric

    KW - PDE

    KW - RootPDE

    KW - Unbinned

    KW - WinPDE

    UR - http://www.scopus.com/inward/record.url?scp=0035873056&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=0035873056&partnerID=8YFLogxK

    U2 - 10.1016/S0010-4655(00)00243-5

    DO - 10.1016/S0010-4655(00)00243-5

    M3 - Article

    VL - 136

    SP - 198

    EP - 207

    JO - Computer Physics Communications

    JF - Computer Physics Communications

    SN - 0010-4655

    IS - 3

    ER -