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Characterizations of Kumaraswamy-geometric distribution
Journal of Statistical Distributions and Applications volume 3, Article number: 1 (2015)
Abstract
Certain characterizations of Kumaraswamy-geometric distribution introduced by Akinsete et al. (JSDA 1:1-21, 2014) are presented.
Introduction
The problem of characterizing a distribution is an important problem in applied sciences, where an investigator is vitally interested to know if their model follows the right distribution. To this end the investigator relies on conditions under which their model would follow specifically chosen distribution. Akinsete et al. (2014) introduced a distribution called Kumaraswamy-geometric distribution (KGD) and studied various properties of the distribution. In this very short note, we present two characterizations of KGD based on: \(\left (i\right) \) Conditional expectation of certain function of the random variable and \(\left (ii\right) \) the reverse hazard rate function.
The cumulative distribution function (cdf) of KGD and its corresponding probability mass function (pmf) are given, respectively, by
and
where q=1−p and p is the parameter of the geometric distribution.
We rewrite \(g\left (x\right) \) as
The hazard rate function of KGD is given by
and its reverse hazard rate function for β=1, by
Characterization results
In what follows we use \(\mathbb {N}^{\ast }\) for \(\left \{ 0\right \} \cup \mathbb {N}\) and present our characterizations via two subsections.
2.1 Characterization of KGD in terms of the conditional expectation of certain function of the random variable
Proposition 2.1.1.
Let \(X:\Omega \rightarrow \mathbb {N}^{\ast }\) be a random variable. The pmf of X is (3) if and only if
Proof.
If X has pmf (3), then the left-hand side of (6) will be
Conversely, if (6) holds, then
From (6), we also have
Now, subtracting (8) from (7), yields
From the above equality, we have
which, in view of (4), implies that X has mpf (3).
Remark 2.1.1.
For β=1, KGD reduces to EEGD (Exponentiated Exponential Geometric Distribution) defined by Alzaatreh et al. (JSM 9:589-603, 2012).
2.2 Characterization of KGD based on reverse hazard function
Proposition 2.2.1.
Let \(X:\Omega \rightarrow \mathbb {N}^{\ast }\) be a random variable. For β=1, the pmf of X is (2) if and only if its reverse hazard rate function satisfies the difference equation
with the initial condition \(r_{g}\left (0\right) =1.\)
Proof.
If X has pmf (2) for β=1, then clearly (9) holds. Now, if (9) holds, then for every \(x\in \mathbb {N}\), we have
or
or
which, in view of the reverse hazard rate function (5), X has pmf (2).
2.3 Further observation
Proposition 2.3.1.
Let X 1,X 2,…,X n be n independent random variables with X i ∼K G D(α,β i ),i=1,2,…,n. Then \(X_{\min }=\min \left \{ X_{1},X_{2},\ldots,X_{n}\right \} \sim KGD\left (\alpha,\sum _{i=1}^{n}\beta _{i}\right).\)
Proof.
It follows from
References
Akinsete, A, Famoye, F, Lee, C: The Kumaraswamy-geometric distribution. JSDA. 1(17), 1–21 (2014).
Alzaatreh, A, Lee, C, Famoye, F: On discrete analogues of continuous distributions. Stat Methodol. 9, 589–603 (2012).
Acknowledgement
The author is grateful to a referee who pointed out an error in Eq. (9) in the original version of this short note. The author thanks an Associate Editor for suggesting the addition of the important second reference.
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Hamedani, G.G. Characterizations of Kumaraswamy-geometric distribution. J Stat Distrib App 3, 1 (2015). https://doi.org/10.1186/s40488-016-0040-5
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DOI: https://doi.org/10.1186/s40488-016-0040-5