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Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations

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As a part of the EUREDIT project new methods to detect multivariate outliers in incomplete survey data have been developed. These methods are the first to work with sampling weights and to be able to cope with missing values. Two of these methods are presented here. The epidemic algorithm simulates the propagation of a disease through a population and uses extreme infection times to find outlying observations. Transformed rank correlations are robust estimates of the centre and the scatter of the data. They use a geometric transformation that is based on the rank correlation matrix. The estimates are used to define a Mahalanobis distance that reveals outliers. The two methods are applied to a small data set and to one of the evaluation data sets of the EUREDIT project.
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Keywords: Data depth; Missing value; Multivariate data; Outlier detection; Robustness; Sampling weight

Document Type: Research Article

Affiliations: 1: University of Neuchâtel, Switzerland. 2: Swiss Federal Statistical Office, Neuchâtel, Switzerland.

Publication date: 2004-05-01

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