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Two activities are essential for characterizing a set of data: ... Examination of the data for unusual observations that are far removed from the mass of data. These points are often referred to as outliers. ... The data set of N = 90 ordered observations as shown below is examined for outliers:
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www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm
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Grubbs' test (Grubbs 1969 and Stefansky 1972) is used to detect outliers in a univariate data set. It is based on the assumption of normality. That is, you should first verify that your data can be reasonably approximated by a normal distribution before applying the Grubbs' test.
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www.itl.nist.gov/div898/handbook/eda/section3/eda35h.ht...
www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm
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In fact, no matter how the data are distributed, Z can not get larger than , where N is the number of values. For example, if N=3, Z cannot be larger than 1.555 for any set of values. ... Outliers in Statistical Data (3rd edition) by V. Barnett and T. Lewis...
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www.graphpad.com/articles/outlier.htm
www.graphpad.com/articles/outlier.htm
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Outlier - Wikipedia, the free encyclopedia
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In statistics, an outlier is an observation that is numerically distant from the rest of the data. Grubbs defined an outlier as: An outlying observation, or outlier, is one that appears to deviate ...
en.wikipedia.org/wiki/Outlier
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The problem is that you can't catch an outlier without a model (at least a mild one) for your data. Else how would you know that a point violated that model? In fact, the process of growing understanding and finding and examining outliers must be iterative.
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www.math.yorku.ca/Lab/outliers.html
www.math.yorku.ca/Lab/outliers.html
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CiteSeerX - Document Details (Isaac Councill, Lee Giles): The focus of this work is on systematic methods for the visualization and quality assessment with regard to classification of multivariate data sets. ... Our novel methods and criteria give in visual and numerical form rapid insight in the principal data distribution,
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citeseer.ist.psu.edu/137312.html
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CiteSeerX - Document Details (Isaac Councill, Lee Giles): We present a methodology for modelling real world high frequency financial data. The methodology copes with the erratic arrival of data and is robust to additive outliers in the data set. ... We present a methodology for modelling real world high frequency financial data.
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citeseer.ist.psu.edu/bolland96robust.html
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The software’s core method of processing is placed in the analysis of every point of the data set, around which a moving window is ... The experience, however, says that traditional tests do tend to be conservative, i.e. to identify as outliers data that could not be so with respect to more realistic distributions.
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www.geodesie.ird.fr/bgi/squelettes/pdf/newton_bulletin/...
www.geodesie.ird.fr/bgi/squelettes/pdf/newton_bulletin/Reasoning_Triglione.pdf
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