Outlier Imputation

Outlier Imputation is a methodological technique within outlier treatment that replaces extreme or anomalous data values with substituted values derived from a specified statistical or machine learning procedure to preserve data integrity and mitigate distortion in downstream analyses.

39

Publications

1.6K

Citations

152

Authors

84

Institutions

Publications per year

2017–2026

21

Authors

152

  1. KS
    Kijung Shin

    Korea Advanced Institute of Science and Technology, South Korea

    2 publications · 41 citations

    2

    h-index

  2. JH
    Jongmin Han

    Sungkyunkwan University, South Korea

    1 publications · 21 citations

    1

    h-index

  3. SL
    Saskia le Cessie

    Leiden University Medical Center, Netherlands

    1 publications · 49 citations

    1

    h-index

  4. NG
    Nophar Geifman

    University of Manchester, United Kingdom

    1 publications · 38 citations

    1

    h-index

  5. HY
    Hong Yeon Cho

    Hanyang University, South Korea

    1 publications · 16 citations

    1

    h-index

1–5 of 152

Venues

  1. BMC Medical Research Methodology

    2 publications · 225 citations

    2

    h-index

  2. Expert Systems with Applications

    1 publications · 21 citations

    1

    h-index

  3. SoftwareX

    1 publications · 16 citations

    1

    h-index

  4. Spatial Economic Analysis

    SEA · 1 publications · 21 citations

    1

    h-index

  5. Journal of Environmental Engineering

    1 publications · 86 citations

    1

    h-index

1–5