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
Parents
32
MissForest—non-parametric missing value imputation for mixed-type data
Daniel J. Stekhoven, Peter Bühlmann · Bioinformatics · 2011 · 6.1K citations · Full text
Comparison of imputation methods for missing laboratory data in medicine
Akbar K. Waljee, Ashin Mukherjee, Amit G. Singal et al. · BMJ Open · 2013 · 500 citations · Full text
Ahmad Alsaber, Jiazhu Pan, Adeeba Al‐Hurban · International Journal of Environmental Research and Public Health · 2021 · 97 citations · Full text
1–5 of 32
116
2
h-index
2
h-index
2
h-index
2
h-index
University Medical Center of the Johannes Gutenberg University Mainz, Germany
1 publications · 16 citations
1
h-index
1–5 of 116
63

University of Strathclyde
Glasgow, United Kingdom
3 h-index
6 publications
346 citations
Kuwait University
Kuwait City, Kuwait
2 h-index
2 publications
140 citations

University of Łódź
Lodz, Poland
2 h-index
2 publications
22 citations
Kansas State University
Manhattan, United States
1 h-index
1 publications
43 citations
Wuhan University
Wuhan, China
1 h-index
4 publications
184 citations

Kwame Nkrumah University of Science and Technology
Kumasi, Ghana
1 h-index
2 publications
90 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
1 h-index
1 publications
46 citations
Kuwait Institute for Scientific Research
Kuwait City, Kuwait
1 h-index
1 publications
69 citations
1–8 of 63
2 publications · 36 citations
2
h-index
2 publications · 64 citations
2
h-index
1 publications · 500 citations
1
h-index
1 publications · 43 citations
1
h-index
1 publications · 27 citations
1
h-index
1–5