Functional Outlier Detection

Functional Outlier Detection is a statistical methodology within functional data analysis that identifies observations whose functional patterns deviate significantly from the majority in a sample of curves, surfaces, or functions, thereby distinguishing them from typical functional data structures.

76

Publications

5.6K

Citations

149

Authors

105

Institutions

Publications per year

2017–2026

34

Authors

149

  1. MG
    Marc G. Genton

    King Abdullah University of Science and Technology, Saudi Arabia

    7 publications · 742 citations

    7

    h-index

  2. JR
    Juan Romo

    Universidad Carlos III de Madrid, Spain

    6 publications · 816 citations

    6

    h-index

  3. YS
    Ying Sun

    King Abdullah University of Science and Technology, Saudi Arabia

    5 publications · 628 citations

    5

    h-index

  4. WD
    Wenlin Dai

    King Abdullah University of Science and Technology, Saudi Arabia

    5 publications · 235 citations

    5

    h-index

  5. JM
    Javier Martínez Martínez

    Universidade de Vigo, Spain

    4 publications · 93 citations

    4

    h-index

1–5 of 149

Venues

  1. Journal of Computational and Graphical Statistics

    JCGS · 6 publications · 901 citations

    6

    h-index

  2. Technometrics

    5 publications · 161 citations

    5

    h-index

  3. Journal of Multivariate Analysis

    4 publications · 170 citations

    4

    h-index

  4. Environmetrics

    4 publications · 356 citations

    4

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 4 publications · 240 citations

    4

    h-index

1–5