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Journal of Automatic Chemistry
Volume 14, Issue 1, Pages 25-27

Technical note: A nonparametric outlier rejection scheme

P.O. Box 151, Terneuzen 4530 AD, The Netherlands

Copyright © 1992 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Experimental data always contains measurement errors (or noise, in signal processing). This paper is concerned with the removal of outliers from a data set consisting of only a handful of points. The data set has a unimodal probability distribution function, the mode is thus a reliable estimate of the central tendency. The approach is nonparametric; for the data set (xi, yi) only the ordinates (yi) are used. The abscissa (xi) are reparametrized to the variable i = 1, N.

The data is bounded using a calculated mode and a new measure: the mean absolute deviation from the mode. This does not seem to have been reported before. The mean is removed and low frequency filtering is performed in the frequency domain, after which the mean is reintroduced.