Abstract
An important task of data analysis consists in identifying a part of the data which represents the typical features of the data generating process, while the remaining data points are seen as less typical or less probable regarding their hypothesized distribution. Often the data points are assumed to vary around a center and a central region is sought which includes the center and reflects the location and general shape of the data. Such a central region can also separate one or more outlying data points from the main body of the distribution. The border of a central region, called contour set, serves as a multivariate quantile.
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© 2002 Springer Science+Business Media New York
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Mosler, K. (2002). Central regions. In: Multivariate Dispersion, Central Regions, and Depth. Lecture Notes in Statistics, vol 165. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0045-8_3
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DOI: https://doi.org/10.1007/978-1-4613-0045-8_3
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-95412-7
Online ISBN: 978-1-4613-0045-8
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