Identification of local multivariate outliers

The Mahalanobis distance between pairs of multivariate observations is used as a measure of similarity between the observations. The theoretical distribution is derived, and the result is used for judging on the degree of isolation of an observation. In case of spatially dependent data where spatial...

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Bibliographic Details
Published inStatistical papers (Berlin, Germany) Vol. 55; no. 1; pp. 29 - 47
Main Authors Filzmoser, Peter, Ruiz-Gazen, Anne, Thomas-Agnan, Christine
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2014
Springer Nature B.V
Subjects
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Finance
Insurance
Management
Mathematics and Statistics
Multivariate analysis
Normal distribution
Operations Research/Decision Theory
Probability Theory and Stochastic Processes
Regular Article
Spatial data
Statistical analysis
Statistics
Statistics for Business
Studies
Robust statistics
Outliers
Spatial dependence
Mahalanobis distance
MCD estimator
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Summary:The Mahalanobis distance between pairs of multivariate observations is used as a measure of similarity between the observations. The theoretical distribution is derived, and the result is used for judging on the degree of isolation of an observation. In case of spatially dependent data where spatial coordinates are available, different exploratory tools are introduced for studying the degree of isolation of an observation from a fraction of its neighbors, and thus to identify local multivariate outliers.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-013-0524-z