Multivariate Outlier Detection

Multivariate Outlier Detection is a methodological framework within outlier detection that identifies anomalous instances in multidimensional data spaces by considering the joint distribution of multiple attributes simultaneously, distinguishing it from univariate or bivariate approaches through its capacity to capture complex, interdependent relationships among variables.

433

Publications

45.3K

Citations

967

Authors

502

Institutions

Publications per year

2017–2026

126

Authors

967

  1. PF
    Peter Filzmoser

    TU Wien, Austria

    14 publications · 2.1K citations

    14

    h-index

  2. PJ
    Peter J. Rousseeuw

    University of Antwerp, Belgium

    10 publications · 3.2K citations

    10

    h-index

  3. AC
    Anthony C. Atkinson

    London School of Economics and Political Science, United Kingdom

    10 publications · 754 citations

    10

    h-index

  4. MR
    Marco Riani

    University of Parma, Italy

    8 publications · 433 citations

    8

    h-index

  5. AC
    Andrea Cerioli

    University of Parma, Italy

    7 publications · 473 citations

    7

    h-index

1–5 of 967

Venues

  1. Journal of the American Statistical Association

    29 publications · 8.9K citations

    24

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 11 publications · 1.2K citations

    11

    h-index

  3. Journal of Computational and Graphical Statistics

    JCGS · 13 publications · 644 citations

    11

    h-index

  4. Technometrics

    11 publications · 1.1K citations

    11

    h-index

  5. 9

    h-index

1–5