International Journal of Statistical Distributions and Applications
Volume 1, Issue 1, September 2015, Pages: 1-4

Characterizations and Infinite Divisibility of Extended COM-Poisson Distribution

Huiming Zhang

School of Mathematics and Statistics, Central China Normal University, Wuhan, China

Email address:

To cite this article:

Huiming Zhang. Characterizations and Infinite Divisibility of Extended COM-Poisson Distribution. International Journal of Statistical Distributions and Applications. Vol. 1, No. 1, 2015, pp. 1-5. doi: 10.11648/j.ijsd.20150101.11

Abstract: This article provides some characterizations of extended COM-Poisson distribution: conditional distribution given the sum, functional operator characterization (Stein identity). We also give some conditions such that the extended COM-Poisson distribution is infinitely divisible, hence some subclass of extended COM-Poisson distributions are discrete compound Poisson distribution.

Keywords: Conway-Maxwell-Poisson distribution, conditional distribution, discrete compound Poisson distribution, infinitely divisible, Stein identity

1. Extended COM-Poisson Distribution

The Conway-Maxwell-Poisson distribution (COM-Poisson distribution) was firstly briefly introduced by Conway and Maxwell (1962) for modeling of queuing systems with state-dependent service times, see Shmueli et al. (2005) for details. The probability mass function (p.m.f.) is given by

where and .

Chakraborty(2015) introduced the extended COM-Poisson distribution from Imoto (2014), Chakraborty and Ong (2014):

Definition A: A r.v. X is said to follow the extended COM-Poisson distribution with parameters [ECOMP ] if its p.m.f. is given by

where the parameter space is


The extended COM-Poisson includes many COM type distributions. For example, COM-Poisson, COM-negative binomial etc., see Chakraborty(2015). It is easy to obtain the p.m.f. of recurrence relation of the ECOMP


with .

The recurrence relation (1) will be useful for arriving some characterizations and properties of the extended COM-Poisson distribution.

There are many statistical models pertain to COM-Poisson for modeling many types of count data, such as COM-Poisson regression (see Guikema and Goffelt (2008)), COM-Poisson in survival analysis (see Rodrigues et al. (2009), Sellers and Shmueli (2010)), COM-Poisson INGARCH time series models (see Zhu (2012)), COM-Poisson distribution chart in statistical process control (see Saghir et al. (2009)), zero-inflated COM-Poisson distribution (see Barriga and Phillips (2014)), fitting data from actuarial science (see Khan and Khan (2010)).

Undoubtedly, extended COM-Poisson distribution with 4 parameters are more flexible than COM-Poisson distribution, negative binomial distribution and so on. It contains a wide range of statistical properties (log-concave and log-convex, under- and over dispersed). Especially, for the phenomenon of over-/equi-/underdispersion, there are many applications in applied statistics, including public health, medicine, and epidemiology, see the literatures review by Kokonendji (2014). So we can consider statistical models above in terms of extended COM-Poisson distribution in the future.

1.1. Functional Operator Characterization

We know the p.m.f. of ECOMP has a simple recurrence relation given by (1). Brown and Xia (2001) studied a very large class of stationary distribution of birth-death process with arrival rate and service rate  by the recursive formula:


Thus we can construct that arrival rate

and service rate

to characterize the ECOMP, , see also Daly and Gaunt (2015). This will be useful for functional characterization in Stein’s method. The following theorem presents a characterization for the ECOMP distributions.

Theorem A: (Stein identity) Let , the r.v. X has distribution  ECOMP iff the equation

holds for any bounded function .

The Theorem A can be directly obtained from the work of Brown and Xia (2001). On the one hand, we just check the expectation is 0, and then the sufficiency is true. On the other hand, to show the necessary, just take function g to be the indicator of {k}, hence we have the recurrence relation (1).

1.2. Conditional Distribution Characterization

For the two independent r.v.’s


the distribution of sum is

So, the conditional distribution of is

We naturally define the extended negative hypergeometric distribution with p.m.f.


where  is the normalization constant.

And we denote (2) as .

Next, we cite a general result by Patil and Seshadri (1964) for characterizing a large class of discrete distributions, see also Kagan et al. (1973). And we obtain the conditional distribution characterization of ECOMP.

Lemma A: Let X and Y be independent discrete r.v.’s and two dimensional function be



where  is a nonnegative function, then


Theorem B: Let X and Y be the independent discrete r.v. with

 and .

If the conditional distribution of is extended negative hypergeometric distribution, then

 ECOMP  and   ECOMP.

Proof: By using Lemma A and the definition of , we have



So, we have



Let, then

Hence we get the result.

2. Log-Convex and Infinitely Divisible

For a discrete r.v. X with p.g.f.

We say that X is infinitely divisible if

where  is the p.g.f. of a certain discrete r.v. .

Feller's characterization of the infinite divisible discrete r.v. (see Feller (1971)) shows that a discrete r.v. is infinitely divisible iff its distribution is a discrete compound Poisson distribution with following p.g.f.

where  is the Lévy measure(or parameters) of a infinitely divisible distribution. It satisfies


Actually, the discrete compound Poisson distribution is the probability distribution of the sum of a number of iid non-negative integer-valued r.v.’s, where the sum is Poisson-distributed r.v..

On the one hand, the discrete compound Poisson(DCP) distributed r.v. can be represented as the sum of n i.i.d. r.v.’s


where  and are independent with


On the other hand, X can be decomposed as sum of weighted Poisson:

where  are independently Poisson distributed with parameter .

For the detailed theoretical and applied treatment of discrete infinitely divisible and discrete compound Poisson, we refer readers to section 2 of Steutel and van Harn (2003), section 9.3 of Johnson et al. (2005), Zhang et al. (2014), Zhang et al. (2013).

A discrete r.v. X with  has log-concave (log-convex) p.m.f. if

Steutel (1970) proved that all log-convex discrete distributions are infinitely divisible, see also Steutel and van Harn (2003). So, it is easy to get infinite divisibility of ECOMP when  satisfy some conditions.

Theorem C: The ECOMP infinitely divisible distribution when  satisfy the following conditions:


Proof: From (1), we have

Then, the equality

holds if


Notice that  goes to its minimum  as . Hence, we need

to make sure the infinite divisibility of ECOMP.

Applying the recurrence relation (Lévy-Adelson-Panjer recursion) of p.m.f. of DCP distribution,


see Buchmann (2003) and the Remark 1 in Zhang et al. (2014), therefore DCP case of ECOMP has the alternative recurrence relation.

The parameters and  of DCP case of ECOMP are determined by the following systems of equation:

where .

The is ECOMP log-concave distribution if satisfy the inequality


Note that  ensures validity of the inequality above. For more theoretical results about general log-concavity of discrete distributions, see Balabdaoui et al. (2013), Saumard and Wellner (2014).


In the author’s future articles, more new characterizations and properties related to this notes in aspects of COM-Poisson distribution will appear, and Extended COM-Poisson distribution in aspects of related statistical models are in progress. If you are interested it, please contact me without hesitation.


  1. Balabdaoui, F., Jankowski, H., Rufibach, K., Pavlides, M. (2013). Asymptotics of the discrete log-concave maximum likelihood estimator and related applications. Journal of the Royal Statistical Society: Series B (StatisticalMethodology), 75(4), 769-790.
  2. Barriga, G. D., Louzada, F. (2014). The zero-inated Conway-Maxwell-Poisson distribution: Bayesian inference, regression modeling and inuence diagnostic. Statistical Methodology, 21, 23-34.
  3. Brown, T. C., Xia, A. (2001). Stein's method and birth-death processes. Annals of probability, 1373-1403.
  4. Buchmann, B., Grbel, R. (2003). Decompounding: an estimation problem for Poisson random sums. The Annals of Statistics,31(4), 1054-1074.
  5. Chakraborty, S. (2015). A new extension of Conway-Maxwell-Poisson distribution and its properties. arXiv preprintarXiv:1503.04443.
  6. Chakraborty, S., Ong, S. H. (2014), A COM-type Generalization of the Negative Binomial Distribution, Accepted in April 2014, to appear in Communications in Statistics-Theory and Methods
  7. Conway, R. W., Maxwell, W. L. (1962). A queuing model with state dependent service rates. Journal of Industrial Engineering, 12(2), 132-136.
  8. Daly, F., Gaunt, R. E. (2015). The Conway-Maxwell-Poisson distribution: distributional theory and approximation.arXiv preprint arXiv:1503.07012.
  9. Feller, W. (1971). An introduction to probability theory and its applications,Vol. I. 3rd., Wiley, New York.
  10. Guikema, S. D.,Goffelt, J. P. (2008). A exible count data regression model for risk analysis. Risk analysis, 28(1), 213-223.
  11. Imoto, T. (2014). A generalized Conway–Maxwell–Poisson distribution which includes the negative binomial distribution. Applied Mathematics and Computation, 247, 824-834.
  12. Kagan, A. M., Linnik, Y. V., Rao, C. R. (1973). Characterization problems in mathematical statistics, Wiley.
  13. Khan, N. M., Khan, M. H. (2010). Model for Analyzing Counts with Over-, Equi-and Under-Dispersion in Actuarial Statistics. Journal of Mathematics and Statistics, 6(2), 92-95.
  14. Kokonendji, C. C. (2014). Over-and Underdispersion Models. Methods and Applications of Statistics in Clinical Trials: Planning, Analysis, and Inferential Methods, Volume 2, 506-526.
  15. Patil, G. P., Seshadri, V. (1964). Characterization theorems for some univariate probability distributions. Journal of the Royal Statistical Society. Series B (Methodological), 286-292.
  16. Sellers, K. F., Shmueli, G. (2010). Predicting censored count data with COM-Poisson regression. Robert H. Smith School Research Paper No. RHS-06-129.
  17. Rodrigues, J., de Castro, M., Cancho, V. G., Balakrishnan, N. (2009). COM-Poisson cure rate survival models and an application to a cutaneous melanoma data. Journal of Statistical Planning and Inference, 139(10), 3605-3611.
  18. Saghir, A., Lin, Z., Abbasi, S. A., Ahmad, S. (2013). The Use of Probability Limits of COM-Poisson Charts and their Applications. Quality and Reliability Engineering International, 29(5), 759-770.
  19. Saumard, A., Wellner, J. A. (2014). Log-concavity and strong log-concavity: a review. Statistics Surveys, 8, 45-114.
  20. Sellers, K. F., Borle, S., Shmueli, G. (2012). The COM-Poisson model for count data: a survey of methods and applications. Applied Stochastic Models in Business and Industry, 28(2), 104-116.
  21. Shmueli, G., Minka, T. P., Kadane, J. B., Borle, S., Boatwright, P. (2005). A useful distribution for ftting discrete data: revival of the Conway-Maxwell-Poisson distribution. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(1),127-142.
  22. Steutel, F. W. (1970). Preservation of infinite divisibility under mixing and related topics. MC Tracts, 33, 1-99.
  23. Steutel, F. W., Van Harn, K. (2003). Infinite divisibility of probability distributions on the real line. CRC Press, New York.
  24. Zhang, H., He J., Huang H. (2013).On nonnegative integer-valued Lévy processes and applications in probabilistic number theory and inventory policies American Journal of Theoretical and Applied Statistics,2 (5), 110-121.
  25. Zhang, H., Liu, Y., Li, B. (2014). Notes on discrete compound Poisson model with applications to risk theory. Insurance: Mathematics and Economics, 59, 325-336.
  26. Zhu, F. (2012). Modeling time series of counts with COM-Poisson INGARCH models. Mathematical and Computer Modelling, 56(9), 191-203.

Article Tools
Follow on us
Science Publishing Group
NEW YORK, NY 10018
Tel: (001)347-688-8931