Iqr-based Detection

IQR-Based Detection is a statistical outlier identification method that defines anomalous observations as data points lying beyond 1.5 times the interquartile range below the first quartile or above the third quartile.

5

Publications

349

Citations

11

Authors

5

Institutions

Publications per year

2017–2026

2

Variants

Tukey's Outlier Detection

Authors

11

  1. VR
    Virender Ranga

    National Institute of Technology Kurukshetra, India

    1 publications · 34 citations

    1

    h-index

  2. TR
    T.N. Rudakov

    Kaliningrad State Technical University, Russia

    1 publications · 17 citations

    1

    h-index

  3. YQ
    Yang Qu

    Harbin Institute of Technology, China

    1 publications · 16 citations

    1

    h-index

  4. CS
    Ch. Sanjeev Kumar Dash

    Yonsei University, South Korea

    1 publications · 262 citations

    1

    h-index

  5. AG
    Ashish Ghosh

    Indian Statistical Institute, India

    1 publications · 262 citations

    1

    h-index

1–5 of 11

Venues

  1. Decision Analytics Journal

    1 publications · 262 citations

    1

    h-index

  2. Physics Letters A

    1 publications · 17 citations

    1

    h-index

  3. Infrared Physics & Technology

    1 publications · 16 citations

    1

    h-index

  4. Cold Spring Harbor Protocols

    1 publications · 20 citations

    1

    h-index

  5. Defence Technology

    1 publications · 34 citations

    1

    h-index

1–5