Simulating non-normal distributions with specified L-moments and L-correlations

Abstract

This paper derives a procedure for simulating continuous non-normal distributions with specified L-moments and L-correlations in the context of power method polynomials of order three. It is demonstrated that the proposed procedure has computational advantages over the traditional product-moment procedure in terms of solving for intermediate correlations. Simulation results also demonstrate that the proposed L-moment-based procedure is an attractive alternative to the traditional procedure when distributions with more severe departures from normality are considered. Specifically, estimates of L-skew and L-kurtosis are superior to the conventional estimates of skew and kurtosis in terms of both relative bias and relative standard error. Further, the L-correlation also demonstrated to be less biased and more stable than the Pearson correlation. It is also shown how the proposed L-moment-based procedure can be extended to the larger class of power method distributions associated with polynomials of order five.