The techniques of exploratory data analysis include a resistant rule, based on a linear combination of quartiles, for the identication 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 improvment occurs in terms of resistance and effciency, and an outside rate that is less aected 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 obtained as special cases of the generalized lambda distribution.
Keywords: Asymptotic effciency; Generalized lambda distribution; Kurtosis; Outside rate; Resistance; Skewness; Small-sample bias