Outlier detection in large data sets

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) nonrobust 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.