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Ein einfaches Verfahren zur Identifikation von Ausreißern bei multivariaten Daten

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  • Buttler, Günter

Abstract

Statistical analysis is often disturbed by objects which are extremely different from the rest of the data. Those outliers can be due to different causes. Therefore it is always recommended to examine them separately. Outliers in one or two-dimensional cases are easily recognized in a frequency distribution. In multidimensional data they can be identified by sub-ordering. It is proposed to do this by calculating pairwise distances. The necessary standardization of the variables can be done by using the sum of all pairwise distances. Proceeding this way possible outliers can easily be identified in tabular as well as in graphical form. It can also be demonstrated which dimension, that is which variables are contributing to the outlier status. As it is quite simple to remove any object or variable, one can see what is happening to the rest of the data without those extreme values.

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  • Buttler, Günter, 1996. "Ein einfaches Verfahren zur Identifikation von Ausreißern bei multivariaten Daten," Discussion Papers 09/1996, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    • Handle: RePEc:zbw:faucse:091996
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    1. Buttler, Günter & Fickel, Norman, 1995. "Clusteranalyse mit gemischtskalierten Merkmalen," Discussion Papers 02/1995, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
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    1. Sascha Kolaric & Zvonimir Pusic & Dirk Schiereck, 2015. "Fußball und Anlegerverhalt en im internationalen Vergleich: Evidenz von 12 Kontinentalmeisterschaften," Schmalenbach Journal of Business Research, Springer, vol. 67(3), pages 297-328, September.
    2. Franz Barjak, 2001. "Regional Disparities in Transition Economies: A Typology for East Germany and Poland," Post-Communist Economies, Taylor & Francis Journals, vol. 13(3), pages 289-311.

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