Resistant outlier rules and the non-Gaussian case

The techniques of exploratory data analysis include a resistant rule, based on a linear combination of quartiles, for identification of outliers. This paper shows that the substitution of the quartiles with the median leads to a better performance in the non-Gaussian case. The improvement occurs in terms of resistance and efficiency, and an outside rate that is less affected by the sample size. The paper also studies issues of practical importance in the spirit of robustness by considering moderately skewed and fat tail distributions obtatined as special cases of the Generalized Lambda Distribution.