Abstract

Asymmetry of a univariate continuous distribution is commonly described as skewness. The well-known classical skewness coefficient is based on the first three moments of the data set, and hence it is strongly affected by the presence of one or more outliers. In this paper we propose several new measures of skewness which are more robust against outlying values. Their properties are compared using both real and simulated data.