Missforest

MissForest is a non-parametric imputation method that iteratively predicts missing values in multivariate data using random forest models trained on observed values, distinguishing itself through its ability to capture complex interactions and non-linear relationships without requiring distributional assumptions.

32

Publications

8.5K

Citations

116

Authors

63

Institutions

Publications per year

2017–2026

27

Authors

116

  1. DJ
    Daniel J. Stekhoven

    ETH Zurich, Switzerland

    2 publications · 6.2K citations

    2

    h-index

  2. AA
    Ahmad Alsaber

    American University of Kuwait, Kuwait

    2 publications · 140 citations

    2

    h-index

  3. MM
    Małgorzata Misztal

    University of Łódź, Poland

    2 publications · 22 citations

    2

    h-index

  4. JP
    Jiazhu Pan

    University of Strathclyde, United Kingdom

    2 publications · 140 citations

    2

    h-index

  5. SR
    Steffen Rapp

    University Medical Center of the Johannes Gutenberg University Mainz, Germany

    1 publications · 16 citations

    1

    h-index

1–5 of 116

Venues

  1. Energies

    2 publications · 36 citations

    2

    h-index

  2. Sensors

    2 publications · 64 citations

    2

    h-index

  3. BMJ Open

    1 publications · 500 citations

    1

    h-index

  4. International Journal of Rheumatic Diseases

    1 publications · 43 citations

    1

    h-index

  5. Frontiers in Public Health

    1 publications · 27 citations

    1

    h-index

1–5