Characterizing distributions by quantile measures
R.T.A. Wagemakers, J.J.A. Moors, Margo Janssens · Data Archiving and Networked Services (DANS) · 1992
Modelling an empirical distribution by means of a simple theoretical distribution is an interesting issue in applied statistics.A reasonable first step in this modelling process is to demand that measures for location, dispersion, skewness and kurtosis for the two distributions coincide.Up to now, the four measures used hereby were based on moments.In this paper measures are considered which are based on quantiles.Of course the four values of these quantile measures do not uniquely determine the modelling distribution.They do, however, within specific systems of distributions, like Pearson's or Johnson's.This opens the possibility of modellingwithin a specific system -an empirical distribution by means of quantile measures.Since momentbased measures are sensitive for outliers, this approach may lead to a better fit.