Kernel-based Mixture Modeling

Kernel-Based Mixture Modeling is a statistical framework within mixture dose-response modeling that employs kernel functions to nonparametrically represent component distributions, thereby enabling flexible estimation of complex response patterns arising from heterogeneous subpopulations.

37

Publications

2.2K

Citations

101

Authors

73

Institutions

Publications per year

2017–2026

8

Authors

101

  1. KB
    Konstantinos Blekas

    University of Ioannina, Greece

    2 publications · 31 citations

    2

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  2. NB
    Nizar Bouguila

    Concordia University, Canada

    2 publications · 144 citations

    2

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  3. TC
    T.F. Cootes

    University of Manchester, United Kingdom

    2 publications · 286 citations

    2

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  4. AT
    Ambuj Tewari

    University of Michigan, United States

    2 publications · 97 citations

    2

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  5. CT
    Chris Taylor

    University of Manchester, United Kingdom

    2 publications · 286 citations

    2

    h-index

1–5 of 101

Venues

  1. 2

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  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 88 citations

    1

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  3. Journal of Statistical Software

    JSS · 1 publications · 40 citations

    1

    h-index

  4. Image and Vision Computing

    1 publications · 254 citations

    1

    h-index

  5. 1

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1–5