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BIVARIATE EXTENSIONS OF SKELLAM'S DISTRIBUTION

Published online by Cambridge University Press:  17 March 2014

Christian Genest
Affiliation:
Department of Mathematics and Statistics, McGill University, 805, rue Sherbrooke ouest, Montréal (Québec)Canada H3A 0B9. E-mail: cgenest@math.mcgill.ca
Mhamed Mesfioui
Affiliation:
Département de mathématiques et d'informatique, Université du Québec à Trois-Rivières, 3351, boulevard des Forges, Trois-Rivières (Québec)Canada G9A 5H7. E-mail: Mhamed.Mesfioui@uqtr.ca

Abstract

Skellam's name is traditionally attached to the distribution of the difference of two independent Poisson random variables. Many bivariate extensions of this distribution are possible, e.g., through copulas. In this paper, the authors focus on a probabilistic construction in which two Skellam random variables are affected by a common shock. Two different bivariate extensions of the Skellam distribution stem from this construction, depending on whether the shock follows a Poisson or a Skellam distribution. The models are nested, easy to interpret, and yield positive quadrant-dependent distributions, which share the convolution closure property of the univariate Skellam distribution. The models can also be adapted readily to account for negative dependence. Closed form expressions for Pearson's correlation between the components make it simple to estimate the para-meters via the method of moments. More complex formulas for Kendall's tau and Spearman's rho are also provided.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Alzaid, A.A. & Omair, M.A. (2010). On the Poisson difference distribution: inference and applications. Bulletin of the Malaysian Mathematical Sciences Society 33: 1745.Google Scholar
2.Bulla, J., Chesneau, C. & Kachour, M. (2012). On the bivariate Skellam distribution. http://hal.archives-ouvertes.fr/hal-00744355Google Scholar
3.Genest, C. & Nešlehová, J. (2007). A primer on copulas for count data. ASTIN Bulletin 37: 475515.CrossRefGoogle Scholar
4.Giveen, S.M. (1963). A taxicab problem with time-dependent arrival rates. SIAM Review 5: 119127.Google Scholar
5.Hwang, Y., Kim, J.-S. & Kweon, I.-S. (2007). Sensor noise modeling using the Skellam distribution: Application to the color edge detection. IEEE Conference on Computer Vision and Pattern Recognition pp. 1–8.CrossRefGoogle Scholar
6.Irwin, W. (1937). The frequency distribution of the difference between two independent variates following the same Poisson distribution. Journal of the Royal Statistical Society 100: 415416.Google Scholar
7.Karlis, D. & Ntzoufras, I. (2003). Analysis of sports data by using bivariate Poisson models. The Statistician 52: 381393.Google Scholar
8.Karlis, D. & Ntzoufras, I. (2006). Bayesian analysis of the differences of count data. Statistics in Medicine 25: 18851905.CrossRefGoogle ScholarPubMed
9.Kendall, D.G. (1951). Some problems in the theory of queues (with discussion). Journal of the Royal Statistical Society Series B 13: 151185.Google Scholar
10.Nelsen, R.B. (2006). An Introduction to Copulas, 2nd ed.Berlin: Springer.Google Scholar
11.Nešlehová, J. (2007). On rank correlation measures for non-continuous random variables. Journal of Multivariate Analysis 98: 544567.Google Scholar
12.Prékopa, A. (1952). On composed Poisson distributions. IV. Remarks on the theory of differential processes. Acta Mathematica Academiae Scientiarum Hungaricae 3: 317325.Google Scholar
13.Romani, J. (1956). Distribución de la suma algebraica de variables de Poisson. Trabajos de Estadística 7: 175181.Google Scholar
14.Skellam, J.G. (1946). The frequency distribution of the difference between two Poisson variates belonging to different populations. Journal of the Royal Statistical Society 109: 296.Google Scholar
15.Strackee, J. & Denier van der Gon, J.J.D. (1962). The frequency distribution of the difference between two Poisson variates. Statistica Neerlandica 16: 1723.Google Scholar