Multivariate Outlier Detection
Multivariate Outlier Detection is a methodological framework within outlier detection that identifies anomalous instances in multidimensional data spaces by considering the joint distribution of multiple attributes simultaneously, distinguishing it from univariate or bivariate approaches through its capacity to capture complex, interdependent relationships among variables.
433
Publications
45.3K
Citations
967
Authors
502
Institutions
Publications per year
2017–2026
126
Parents
433
<i>Outliers in Statistical Data</i>
V. Barnett, T. Lewis, Francine F. Abeles · Physics Today · 1979 · 2.8K citations
MVN: An R Package for Assessing Multivariate Normality
Selçuk Korkmaz, Dinçer Göksülük, Gökmen Zararsız · The R Journal · 2014 · 1.3K citations · Full text
1–5 of 433
967
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London School of Economics and Political Science, United Kingdom
10 publications · 754 citations
10
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8
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7
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1–5 of 967
502
KU Leuven
Leuven, Belgium
15 h-index
38 publications
4.4K citations

TU Wien
Vienna, Austria
13 h-index
23 publications
2K citations

University of Parma
Parma, Italy
12 h-index
26 publications
1.4K citations
London School of Economics and Political Science
London, United Kingdom
11 h-index
12 publications
865 citations

Universidad Carlos III de Madrid
Madrid, Spain
11 h-index
18 publications
2.3K citations
Vrije Universiteit Brussel
Brussels, Belgium
7 h-index
12 publications
465 citations
TU Dortmund University
Dortmund, Germany
6 h-index
10 publications
659 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
6 h-index
8 publications
319 citations
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29 publications · 8.9K citations
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CSDA · 11 publications · 1.2K citations
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JCGS · 13 publications · 644 citations
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11 publications · 1.1K citations
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9 publications · 244 citations
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