Summary
Consider a sum of independent and identically distributed random vectors with finite second moments, where the number of terms has a geometric distribution independent of the summands. We show that the class of limiting distributions of such random sums, as the number of terms converges to infinity, consists of multivariate asymmetric distributions that are natural generalizations of univariate Laplace laws. We call these limits multivariate asymmetric Laplace laws. We give an explicit form of their multidimensional densities and show representations that effectively facilitate computer simulation of variates from this class. We also discuss the relation to other formerly considered classes of distributions containing Laplace laws.
Similar content being viewed by others
Bibliography
Anderson, D. N. (1992). A Multivariate Linnik Distribution, Statistics \; Probability Letters, 14, 333–336.
Ernst, M. D. (1998). A Multivariate Generalized Laplace Distribution, Computational Statistics, 13, 227–232.
Hinkley, D.V. and Revankar, N. S. (1977). Estimation of the Pareto Law from Underreported Data, Journal of Econometrics, 5, 1–11.
Johnson, M. E. (1987). Multivariate Statistical Simulation, New York: John Wiley \; Son, Inc.
Johnson, N. L. and Kotz, S. (1970). Continuous Univariate Distributions, vol. 1–2, Boston: Houghton Mifflin Company.
Kozubowski, T. J. and Podgórski, K. (1998a). Asymmetric Laplace Distributions, Mathematical Scientist, to appear.
Kozubowski, T. J. and Podgórski, K. (1998b). Asymmetric Multivariate Laplace Laws, submitted.
Madan, D. B., Carr, P. and Chang, E. C. (1998). The Variance Gamma Process and Option Pricing, Working Paper, University of Maryland, College Park, MD 20742.
McGraw, D. K. and Wagner, J. F. (1968). Elliptically Symmetric Distributions, IEEE Transactions on Information Theory, 14, 110–120.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Kozubowski, T.J., Podgórski, K. A Multivariate and Asymmetric Generalization of Laplace Distribution. Computational Statistics 15, 531–540 (2000). https://doi.org/10.1007/PL00022717
Published:
Issue Date:
DOI: https://doi.org/10.1007/PL00022717