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
39
A comparative study of evaluating missing value imputation methods in label-free proteomics
Liang Jin, Yingtao Bi, Chenqi Hu et al. · Scientific Reports · 2021 · 170 citations · Full text
1–5 of 39
152
Korea Advanced Institute of Science and Technology, South Korea
2 publications · 41 citations
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1–5 of 152
84
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
2 h-index
4 publications
82 citations
KU Leuven
Leuven, Belgium
2 h-index
6 publications
186 citations

University of Toronto
Toronto, Canada
1 h-index
3 publications
84 citations
Middle East Technical University
Ankara, Türkiye
1 h-index
1 publications
20 citations

Bilkent University
Ankara, Türkiye
1 h-index
2 publications
40 citations
Pontificia Universidad Javeriana
Bogotá, Colombia
1 h-index
1 publications
12 citations
Health Sciences Centre
Winnipeg, Canada
1 h-index
3 publications
84 citations
Princess Nourah bint Abdulrahman University
Riyadh, Saudi Arabia
1 h-index
3 publications
78 citations
1–8 of 84
2 publications · 225 citations
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1 publications · 21 citations
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1 publications · 16 citations
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SEA · 1 publications · 21 citations
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1 publications · 86 citations
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