Mardia's test is based on multivariate extensions of skewness and kurtosis measures. In a subsequent paper Pearson reported that for any distribution skewness + 1 < kurtosis. This includes identifying outliers, skewness, kurtosis, a need for transformations, and mixtures. Empirical asset returns distributions, however, tend to exhibit fat-tails (kurtosis) and skew. Distributions with zero excess kurtosis are called mesokurtic, or mesokurtotic. This approximation also suffers from the same problem as kurtosis (sensitivity to outliers). Distributions with kurtosis greater than 3 are said to be "leptokurtic". Another approach is using negentropy instead of kurtosis. It resembles the normal distribution in shape but has heavier tails (higher kurtosis). The degrees of freedom is sometimes known as the "kurtosis parameter". Bajgier and Aggarwal have proposed a test based on the kurtosis of the distribution. It is common to compare the kurtosis of a distribution to this value. Several letters are used in the literature to denote the kurtosis. In 1986 Moors gave an interpretation of kurtosis. The kurtosis in both these cases is 1.