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Exponentiated MarshallOlkin family of distributions
Journal of Statistical Distributions and Applications volume 3, Article number: 15 (2016)
Abstract
We study general mathematical properties of a new class of continuous distributions with three extra shape parameters called the exponentiated MarshalOlkin family of distributions. Further, we present some special models of the new class and investigate the shapes and derive explicit expressions for the ordinary and incomplete moments, quantile and generating functions and probability weighted moments. We discuss the estimation of the model parameters by maximum likelihood and show empirically the potentiality of the family by means of two applications to real data.
Introduction
In the past few years, several ways of generating new distributions from classic ones were developed and discussed. Eugene et al. (2002) defined a class of betagenerated distribution. Jones (2004) studied a family of distributions that arises naturally from the distribution of the order statistics and introduced general properties of the proposed class of distributions. Zografos and Balakrishnan (2009) proposed the gammagenerated family of distributions. Later, Cordeiro and de Castro (2011) defined the Kumaraswamy family. Recently, Alzaatreh et al. (2013) proposed a new technique to derive wider families by using any probability density function (pdf) as a generator. This generator called the TX family of distributions has cumulative distribution function (cdf) defined by
where G(x) is the cdf of a random variable X, r(t) is the pdf of a random variable T defined on [a,b] and W[G(x)] is a function of G(x), which satisfies the following conditions:

W[G(x)]∈[a,b];

W[G(x)] is differentiable and monotonically nondecreasing;

W[G(x)]→a as x→−∞ and W[G(x)]→b as x→∞.
Following Alzaatreh et al. (2013) and replacing r(t) by the generalized exponentialgeometric (GEG) density function (Silva et al. 2010), where T∈[0,∞), and using W[G(x)]=− log[1−G(x)], we define the cdf of a new wider family by
where G(x;ξ) is the baseline cdf depending on a parameter vector ξ and α>0, λ>0 and p<1 are three additional shape parameters. For each baseline G, the exponentiated MarshallOlkinG (“EMOG” for short) distribution is defined by the cdf (2). The EMO family includes as special cases the exponentiated generalized class of distributions (Cordeiro et al. 2013), the proportional and reversed hazard rate models, the MarshallOlkin family and other subfamilies. Some special models are listed in Table 1, where G(x)=G(x;ξ).
Furthemore, the basic motivations for using the EMOG family in practice are the following:

i.
to make the kurtosis more flexible compared to the baseline model;

ii.
to produce a skewness for symmetrical distributions;

iii.
to construct heavytailed distributions for modeling real data;

iv.
to generate distributions with symmetric, leftskewed, rightskewed or reversedJ shape;

v.
to define special models with all types of the hrf;

vi.
to provide consistently better fits than other generated models under the same baseline distribution.
This paper is organized as follows. In Section 2, we define the new family of distributions and provide a physical interpretation. Five of its special distributions are discussed in this section. In Section 3, some properties of the EMOG family are presented. The shape of the density and hazard rate functions are described analytically, two useful linear mixtures are provided. We derive a power series for the quantile function (qf) and we provide two general formulae for the moments. The incomplete moments are investigated and we derive the moment generating function (mgf) and determine the mean deviations. Estimation of the model parameters by maximum likelihood is performed in Section 4. Applications to two real data sets illustrate the performance of the EMO family in Section 5. The paper is concluded in Section 6.
The new family
The density function corresponding to (2) is given by
where g(x;ξ) is the baseline pdf. This density function will be most tractable when the functions G(x) and g(x) have simple analytic expressions. Hereafter, a random variable X with density function (3) is denoted by X∼EMOG(p,α,λ,ξ). Henceforth, we can omit sometimes the dependence on the baseline vector ξ of parameters and write simply G(x)=G(x;ξ), f(x)=f(x;p,α,λ,ξ), etc.
A physical interpretation of the EMOG distribution can be given as follows. Consider a system formed by α independent components having the MarshallOlkin cdf given by
Suppose the system fails if all of the α components fail and let X denote the lifetime of the entire system. Then, the cdf of X is
The hazard rate function (hrf) of X becomes
The EMO family of distributions is easily simulated by inverting (2): if u has a uniform U(0,1) distribution, the solution of the nonlinear equation
follows the density function (3).
2.1 Special EMO distributions
For p=0, we obtain, as a special case of (3), the exponentiated generalized class (Cordeiro et al. 2013) of distributions, which provides greater flexibility of its tails and can be applied in many areas of engineering and biology. Here, we present some special cases of the EMO family since it extends several useful distributions in the literature. For all cases listed below, p∈(0,1), α>0 and λ>0. These cases are defined by taking G(x) and g(x) to be the cdf and pdf of a specified distribution. The general form of the pdf of the special EMO distributions can be expressed as:
where q(θ _{1},θ _{2},…,θ _{ m }) is defined as a function of m parameters of the special EMO distribution. We list some special EMO distributions in Table 2, where the letters N, Fr, Ga, B, and Gu stand for the normal, Fréchet, gamma, beta and Gumbel baselines, respectively.
For the EMON distribution, the parameter σ has the same dispersion property as in the normal density. For the EMOB distribution, the beta distribution corresponds to the limiting case: p→0 and α=λ=1. For the EMOFr distribution, we have the classical Fréchet distribution when p=0 and α=λ=1. The Kumaraswamy beta (KwB) and Kumaraswamygamma (KwGa) distributions can be obtained from the EMOB and EMOGa models when p→0. Plots of these EMOG density functions are displayed in Figs. 1, 2, 3, 4 and 5.
Some properties of the EMOG family
We investigate some properties of the EMOG in this section.
3.1 Asymptotic and shapes
Proposition 1
Let a= inf{x G(x)>0}. The asymptotics of Eqs. (2), (3) and (4) when x→a are given by
Proposition 2
The asymptotics of Eqs. (2), (3) and (4) when x→∞ are given by
The shapes of the density and hazard rate functions can be described analytically. The critical points of the EMOG density function are the roots of the equation
that corresponds to points where f ^{′}(x)=0. There may be more than one root to (6). Let λ(x)=d ^{2} log[f(x)]/d x ^{2}. We have
If x=x _{0} is a root of (6) then it corresponds to a local maximum if λ(x)>0 for all x<x _{0} and λ(x)<0 for all x>x _{0}. It corresponds to a local minimum if λ(x)<0 for all x<x _{0} and λ(x)>0 for all x>x _{0}. It gives a point of inflexion if either λ(x)>0 for all x≠x _{0} or λ(x)<0 for all x≠x _{0}.
The critical points of the (hrf) of X are obtained from the equation
There may be more than one root to (8). Let τ(x)=d ^{2} log[h(x)]/d x ^{2}. If x=x _{0} is a root of (8) then it refers to a local maximum if τ(x)>0 for all x<x _{0} and τ(x)<0 for all x>x _{0}. It corresponds to a local minimum if τ(x)<0 for all x<x _{0} and τ(x)>0 for all x>x _{0}. It gives an inflexion point if either τ(x)>0 for all x≠x _{0} or τ(x)<0 for all x≠x _{0}.
3.2 Linear mixtures
We can demonstrate that the cdf (2) of X admits the expansion
where \(b_{k}=\sum \limits _{i,j=0}^{\infty }w_{i,j,k}\),
and H _{ k }(x;ξ)=G(x;ξ)^{k} denotes the exponentiatedG (“expG”) cdf with power parameter k.
The density function of X can be expressed as an infinite linear mixture of expG density functions
where (for k≥0) h _{ k+1}(x;ξ)=(k+1) g(x;ξ) G(x;ξ)^{k} denotes the density function of the random variable Y _{ k+1}∼expG(k+1). Equation (10) reveals that the EMOG density function is a linear mixture of expG density functions. Thus, some of its mathematical properties can be derived directly from those properties of the expG distribution. For example, the ordinary and incomplete moments and (mgf) of X can be obtained from those quantities of the expG distribution. Some structural properties of the expG distributions are welldefined by Mudholkar and Hutson (1996), Gupta and Kundu (2001) and Nadarajah and Kotz (2006), among others.
The formulae derived throughout the paper can be easily handled in most symbolic computation software platforms such as Maple, Mathematica and Matlab. These platforms have currently the ability to deal with analytic expressions of formidable size and complexity. Established explicit expressions to calculate statistical measures can be more efficient than computing them directly by numerical integration. The infinity limit in these sums can be substituted by a large positive integer such as 20 or 30 for most practical purposes.
3.3 Quantile power series
We obtain explicit expressions for the moments and generating function of the EMO family using a power series for the qf x=Q(u)=F ^{−1}(u) of X by expanding (5). If the G qf, say Q _{ G }(u), does not have a closedform expression, this function can usually be expressed as a power series
where the coefficients a _{ i }’s are suitably chosen real numbers depending on the parameters of the parent distribution. For several important distributions such as the normal, Student t, gamma and beta distributions, Q _{ G }(u) does not have explicit expressions but it can be expanded as in Eq. (11).
We use throughout the paper a result of Gradshteyn and Ryzhik (2000) for a power series raised to a positive integer n (for n≥1)
where the coefficients c _{ n,i } (for i=1,2,…) are easily determined from the recurrence equation, with \(c_{n,0}={a_{0}^{n}}\),
Clearly, c _{ n,i } can be easily evaluated numerically from c _{ n,0},…,c _{ n,i−1} and then from the quantities a _{0},…,a _{ i }.
Next, we derive an expansion for the argument of Q _{ G }(·) in Eq. (5)
Using the the generalized binomial expansion four times since u∈(0,1), we can write
Then, the qf of X can be expressed from (5) as
where
By combining (11) and (14), we have
where \(e_{m}={\sum \nolimits }_{i=0}^{\infty } a_{i}\,d_{i,m}\), \(d_{i,0}={\delta _{0}^{i}}\) and, for m>1,
Equation (15) is the main result of this section. It allows to obtain various mathematical quantities for the EMOG family as can be seen in the next sections. Note that
where f _{ r,m } is obtained from the e _{ m }’s using (13).
The effects of the shape parameters on the skewness and kurtosis can be determined from quantile measures. The shortcomings of the classical kurtosis measure are wellknown. The Bowley skewness (Kenney and Keeping 1962) is one of the earliest skewness measures defined by the average of the quartiles minus the median divided by half the interquartile range, namely
Since only the middle two quartiles are considered and the outer two quartiles are ignored, this adds robustness to the measure. The Moors kurtosis (Moors 1998) is based on octiles
These measures are less sensitive to outliers and they exist even for distributions without moments. In Figs. 6 and 7, we plot the measures B and M for the EMOFr and EMON distributions (discussed in Section 2), respectively. These plots reveal how both measures B and M vary on the shape parameters.
3.4 Moments
Hereafter, we shall assume that G is the cdf of a random variable Z and that F is the cdf of a random variable X having density function (3). The rth ordinary moment of X can be obtained from the (r,k)th Probability Weighted Moment (PWM) of Z defined by
In fact, we have
Thus, the moments of any EMOG distribution can be expressed as an infinite linear combination of the PWMs of G. A second formula for τ _{ r,k } can be based on the parent qf Q _{ G }(u)=G ^{−1}(u). Setting G(x)=u, we obtain
where the integral follows from (16) as
The PWMs for some wellknown distributions will be determined in the following sections using alternatively Eqs. (17) and (19).
The central moments (μ _{ s }) and cumulants (κ _{ s }) of X can be obtained from Eqs. (18) and (20) as
where \(\kappa _{1}=\mu ^{\prime }_{1}\). The skewness and kurtosis measures can be calculated from the ordinary moments using wellknown relationships. The pth descending factorial moment of X is
where s(r,k)=(k!)^{−1}[d ^{k} x ^{(r)}/d x ^{k}]_{ x=0} is the Stirling number of the first kind. So, we can obtain the factorial moments from the ordinary moments given before.
3.5 Incomplete moments
The nth incomplete moment of X is defined as \(m_{r}(y)= \int _{\infty }^{y}x^{r}\,f(x)dx\). For empirical purposes, the shape of many distributions can be usefully described by the incomplete moments. Here, we propose two methods to determine the incomplete moments of the new family. First, we can express m _{ r }(y) as
The integral in (21) can be evaluated at least numerically for most baseline distributions.
A second method for the incomplete moments of X follows from (21) using Eqs. (12) and (13). We obtain
Equations (21) and (22) are the main results of this section.
3.6 Generating function
Here, we provide three formulae for the mgf M(s)=E(e^{sX}) of X. A first formula for M(s) comes from Eq. (10) as
where M _{ k+1}(s) is the generating function of the expG(k+1) distribution. Hence, M(s) can be determined from an infinite linear combination of the expG generating functions.
A second formula for M(s) can be derived from Eq. (10) as
where
We can derive the mgfs of several EMO distributions directly from Eqs. (24) and (25). For example, the mgfs of the exponentiated MarshallOlkin exponential (EMOE) (such that λ s<1) and EMOstandard logistic (for s<1) distributions are given by
respectively.
3.7 Mean deviations
The mean deviations about the mean (\(\delta _{1}=E(X\mu ^{\prime }_{1})\)) and about the median (δ _{2}=E(X−M)) of X can be expressed as
respectively, \(F(\mu ^{\prime }_{1})\) is easily evaluated from Eq. (2),
is the median of X, \(\mu ^{\prime }_{1}=\mathrm {E}(X)\) comes from (18) and m _{1}(y) is the first incomplete moment of X determined from (22) with r=1.
Next, we provide three alternative ways to compute δ _{1} and δ _{2}. A general equation for m _{1}(z) is given by (21). A second general formula for m _{1}(z) can be obtained from (10) as
where
Equation (28) is the basic quantity to compute the mean deviations for the expG distributions. A simple application of (27) and (28) can be conducted to the exponentiated MarshallOlkin Weibull (EMOW) distribution. The exponentiated Weibull density function (for x>0) with power parameter k+1, shape parameter c and scale parameter β is given by
and then
The last integral reduces to the incomplete gamma function
where \(\gamma (a,x)= {\int _{0}^{x}} w^{a1}\,\mathrm {e}^{w}\mathrm {d}w\).
A third general formula for m _{1}(z) can be derived by setting u=G(x) in (10)
where T _{ k }(z) is given by
Applications of these equations are straightforward to obtain Bonferroni and Lorenz curves. These curves are defined (for a given probability π) by \(B(\pi)=m_{1}(q)/(\pi \,\mu ^{\prime }_{1})\) and \(L(\pi)=m_{1}(q)/\mu ^{\prime }_{1}\), respectively, where q=F ^{−1}(π)=Q(π) comes from the qf of X for a given probability π.
Estimation
Several approaches for parameter estimation were proposed in the literature but the maximum likelihood method is the most commonly employed. The maximum likelihood estimators (MLEs) enjoy desirable properties and can be used when constructing confidence intervals for the model parameters. The normal approximation for these estimators in large samples can be easily handled either analytically or numerically. So, we consider the estimation of the unknown parameters for this family from complete samples only by maximum likelihood. Here, we determine the MLEs of the parameters of the new family of distributions from complete samples only.
Let x _{1},…,x _{ n } be the observed values from the EMOG distribution with parameters p,α,λ and ξ. Let θ=(p,α,λ,ξ)^{⊤} be the r×1 parameter vector. The total loglikelihood function for θ is given by
The maximized loglikelihood can be either directly by using the NLMIXED procedure in SAS or the subroutine MaxBFGS in the Ox program (see Doornik 2009) or by solving the nonlinear likelihood equations obtained by differentiating (31). The components of the score function
are given by
and
where h ^{(ξ)}(·) means the derivative of the function h with respect to ξ. For interval estimation of the model parameters, we can derive the observed information matrix J _{ n }(θ), whose elements can be obtained from the authors upon request. Let \(\widehat {\theta }\) be the MLE of θ. Under standard regularity conditions (Cox and Hinkley 1974), we can approximate the distribution of \(\sqrt {n}(\widehat {\theta }  \theta)\) by the multivariate normal N _{ r }(0,K(θ)^{−1}), where \(K(\theta) = {\lim }_{n \rightarrow \infty }n^{1}J_{n}(\theta)\) is the unit information matrix and r is the number of parameters of the new distribution.
Often with lifetime data and reliability studies, one encounters censoring. In a very realistic random censoring mechanism, each individual i is assumed to have a lifetime X _{ i } and a censoring time C _{ i }, where X _{ i } and C _{ i } are independent random variables. Suppose that the data consist of n independent observations x _{ i }=min(X _{ i },C _{ i }) and δ _{ i }=I(X _{ i }≤C _{ i }) is such that δ _{ i }=1 if X _{ i } is a time to event and δ _{ i }=0 if it is right censored for i=1,…,n. The censored likelihood L(θ) for the model parameters is
where f(x;p,α,λ,ξ) is given by (3) and S(x;p,α,λ,ξ) is the survival function evaluated from (2).
Applications
In this section, we use a real data set, collected by Prater (1956) and analyzed by Atkinson (1985), on the stress among women in Townsville, Queensland, Australia and the proportion of crude oil converted to gasoline after distillation and fractionation. This application aims to illustrate the potentiality of the EMO family. All the computations were done using the R software. For this application, we consider the following distributions: EMOB (a,b,p,λ,α) and KwWP(a,b,c,λ,β) (Kwmaraswamy Weibull Poisson distribution) proposed by Ramos et al. (2015), both with five parameters. The density of the KwWP is given by
A descriptive analysis of the data is presented in Table 3.
It is possible to obtain qualitative information about the hrf by means of plot analysis when we have the data censored or uncensored. We emphasize that the data sets here are uncensored. For this type of data, the total time in test (TTT) plot proposed by Aarset (1987) may be used. Let T be a random variable with nonnegative values that represents the survival time. The TTT curve is constructed by plotting the statistic \(G(r/n)=\left [\sum \limits _{i=1}^{r}T_{i:n}+(n+r)T_{r:n}\right ]/\left (\sum \limits _{i=1}^{n}T_{i:n}\right)\) versus r/n (r=1,…,n), where the values T _{ i:n } are the order statistics of the sample, for i=1,…,n. The plots can be easily obtained using the TTT function of the AdequacyModel package from the R software. More details about this package are available from help(TTT). The TTT plots for the dataset in this application are shown in Fig. 8. For both plots, the TTT curve is concave, which, according to Aarset (1987), provides evidence that a monotonic increasing hrf is adequate.
The Fig. 9 displays the fitted densities to the current data obtained in a nonparametric manner using the gaussian kernel density estimation, defined as follows. Let X _{1},…,X _{ n } be a random vector of random variables independent and identically distributed where each variable follows an unknown distribution, denoted by f. The kernel density estimator is given by the following expression
where K(·) is the symmetrical kernel function and \(\int \limits _{\infty }^{\infty }K(x)dx=1\). Furthermore, h>0 is known in literature as bandwith, which is a smoothing parameter. It is possible to find in literature numerous kernel function, as the normal standard distribution, for example. Silverman (1986) demonstrated that for the K standard normal, a reasonable bandwith is given by \(h=\sqrt [5]{\left (4\hat {\sigma }^{5}/3n\right)}\approx 1.06\hat {\sigma }/\sqrt [5]{n}\), where \(\hat {\sigma }\) is defined by the standard deviation of the sample.
The plots displayed in Fig. 10 indicates that the EMOB distribution provides the best fit compared with the other fitted distributions. We note the good adequacy of the fitted EMOB distribution in Fig. 11.
In this application, we use the package AdequacyModel. This package is intended to provide a computational support to work with probability distributions, mainly distributions aimed to survival analysis. This package was used to calculate some fitness statistics adjustment such as AIC (Akaike Information Criterion), CAIC (Consistent Akaikes Information Criterion), BIC (Bayesian Information Criterion), HQIC (HannanQuinn information criterion), KS (Test of KolmogorovSmirnov), A ^{∗} (statistic of AndersonDarling) and W ^{∗} (statistic of Cramérvon Mises), which are described by Chen and Balakrishnan (1995), based on the results presented by Stephens (1986). When we want to test if one random sample, denoted by x _{1},x _{2},…,x _{ n }, with empirical distribuction function F _{ n }(x) comes from a specific distribution, we use these statistics. The Cramérvon Mises (W ^{∗}) and AndersonDarling (A ^{∗}) statistics are given by the following expressions:
respectively. In these expressions, we have that F _{ n }(x) is the empirical distribution function, \(F(x;\hat {\theta }_{n})\) is the postulated distribution function evaluated at the MLE of θ, i.e. \(\hat {\theta }_{n}\). Lower values of W ^{∗} and A ^{∗} provide evidence that \(F(x;\hat {\theta }_{n})\) generates the sample. More details about these statistics are given by Chen and Balakrishnan (1995). The goodness.fit function is used to calculate these statistics. More details can be obtained using the command help(goodness.fit). The Table 4 shows the goodnessoffit statistics (rounding to the fourth decimal place) for the dataset used in this application. The results showed that the EMOB (a,b,p,λ,α) distribution presented better results for the KS, A ^{∗} and W ^{∗} statistics when compared with the other distributions used in this application.
In this study, the MLEs in Table 5 were obtained by global search heuristic method called Particle Swarm optmization  PSO proposed by Eberhart and Kennedy (1995). One of the advantages of using the PSO method in addition to being a robust optimization method is that there is no need to provide initial guesses. However, this is a computationally intensive method. The Appendix A shows the function pso implemented in R. At the end of the code there is a small example of how to use the function to minimize an objective function. The standard errors of the MLEs can be obtained by the bootstrap method. The standard errors were not obtained in these examples due to the use of the PSO method, which is computationally intensive.
Conclusions
We derive general mathematical properties of a new continuous distributions with three extra shape parameters. We present some special models of the new EMO family of distributions. We investigate the shapes and derive explicit expressions for the ordinary and incomplete moments, quantile and generating functions and probability weighted moments, which hold for any baseline model. The estimation of the model parameters is by the method of maximum likelihood and the estimates were obtained by global search heuristic method called Particle Swarm OptmizationPSO. Ultimately, we fit some EMOG distributions to two real data sets to demonstrate the potentiality of this family. We hope this generalization may attract wider applications in statistics.
Appendix
Code in R language for PSO method
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Authors’ contributions
The authors, CRBD, GMC, MA, PRDM and HFCC with the consultation of each other carried out this work and drafted the manuscript together. All authors read and approved the final manuscript.
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The authors declare that they have no competing interests.
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B. Dias, C.R., Cordeiro, G.M., Alizadeh, M. et al. Exponentiated MarshallOlkin family of distributions. J Stat Distrib App 3, 15 (2016). https://doi.org/10.1186/s4048801600512
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Keywords
 Generalized exponential geometric distribution
 Generated family
 Maximum likelihood
 Moment