Some characterizations and properties of COM-Poisson random variables
被引:10
|
作者:
Li, Bo
论文数: 0引用数: 0
h-index: 0
机构:
Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R ChinaCent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China
Li, Bo
[1
]
Zhang, Huiming
论文数: 0引用数: 0
h-index: 0
机构:
Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China
Peking Univ, Ctr Stat Sci, Beijing, Peoples R ChinaCent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China
Zhang, Huiming
[2
,3
]
He, Jiao
论文数: 0引用数: 0
h-index: 0
机构:
Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R ChinaCent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China
He, Jiao
[1
]
机构:
[1] Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China
[2] Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China
[3] Peking Univ, Ctr Stat Sci, Beijing, Peoples R China
Conway-Maxwell-Poisson distribution;
conditional distribution;
recurrence formula;
Fisher information for discrete distribution;
Stam inequality;
closed under addition;
BINOMIAL-DISTRIBUTION;
OVERDISPERSION;
DISTRIBUTIONS;
D O I:
10.1080/03610926.2018.1563164
中图分类号:
O21 [概率论与数理统计];
C8 [统计学];
学科分类号:
020208 ;
070103 ;
0714 ;
摘要:
Starting with a literature review for theoretical properties of COM-Poisson distributions, this paper proposes some new characterizations of COM-Poisson random variables. First, we extend the Moran-Chatterji characterization and generalize the Rao-Rubin characterization of Poisson distribution to COM-Poisson distribution. Then, we define the COM-type discrete r.v. of the discrete random variable X. The probability mass function of has a link to the Renyi entropy and Tsallis entropy of order nu of X. And then we can get the characterization of Stam inequality for COM-type discrete version Fisher information. By using the recurrence formula, the property that COM-Poisson random variables () is not closed under addition is obtained. Finally, under the property of "not closed under addition" of COM-Poisson random variables, a new characterization of Poisson distribution is found.