Outlier detection in large data sets

doi:10.1016/j.compchemeng.2010.11.004

Outlier detection in large data sets

2011-02-01

60

PRE

AI

G

Guido Buzzi‐Ferraris *

F

Flavio Manenti

DOI：10.1016/j.compchemeng.2010.11.004

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In this paper we propose a method for correctly detecting outliers based on a new technique developed to simultaneously evaluate mean, variance and outliers. This method is capable of self-regulating its robustness to suit the experimental data set under analysis, so as to overcome shortcomings of: (i) non-robust methods such as the least sum of squares; (ii) the need of the user in defining a trimmed sub-set of experimental points such as in least trimmed sum of squares; and (iii) the possibility to read the data set only once to evaluate the mean, variance, and outliers of a population by preserving robustness. (C) 2010 Published by Elsevier Ltd.

Keyword:

Outliers

Reliable parameter estimation

Robustness

Large data sets

Outlier detection in large data sets

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