A method of generating multivariate non-normal random numbers with desired multivariate skewness and kurtosis

An Elegant Method for Generating Multivariate Poisson Random Variables

Generating multivariate Poisson data is essential in many applications. Current simulation methods suffer from limitations ranging from computational complexity to restrictions on the structure of the correlation matrix. We propose a computationally efficient and conceptually appealing method for generating multivariate Poisson data. The method is based on simulating multivariate Normal data an...

Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and edu...

Screening for a Multivariate Mixture Normal Distribution

Nonparametric Multivariate Kurtosis and Tailweight Measures

For nonparametric exploration or description of a distribution, the treatment of location, spread, symmetry and skewness is followed by characterization of kurtosis. Classical momentbased kurtosis measures the dispersion of a distribution about its “shoulders”. Here we consider quantile-based kurtosis measures. These are robust, are defined more widely, and discriminate better among shapes. A u...

Random palindromes: multivariate generating function and Bernoulli density

Consider a nite alphabet with a probability distribution p. We study the probability (p) of obtaining a palindrome in a nite time by independent draws. Using a Mahler equation for an associated generating function, we give a closed form expression for (p). Moreover we describe completely the cases where (p) has value less than 1, in connection with the singularities of the generating function. ...