 Research
 Open Access
The McDonald exponentiated gamma distribution and its statistical properties
 Abdulhakim A AlBabtain^{1}Email author,
 Faton Merovci^{2}Email author and
 Ibrahim Elbatal^{3}Email author
https://doi.org/10.1186/2193180142
© Albabtain et al.; licensee Springer. 2015
 Received: 20 November 2014
 Accepted: 15 December 2014
 Published: 12 February 2015
Abstract
Abstract
In this paper, we propose a fiveparameter lifetime model called the McDonald exponentiated gamma distribution to extend beta exponentiated gamma, Kumaraswamy exponentiated gamma and exponentiated gamma, among several other models. We provide a comprehensive mathematical treatment of this distribution. We derive the moment generating function and the rth moment. We discuss estimation of the parameters by maximum likelihood and provide the information matrix.
AMS Subject Classification
Primary 62N05; secondary 90B25
Keywords
 McDonald exponentiated gamma distribution
 Moments
 Exponentiated gamma distribution
 Order statistics
 Maximum likelihood estimation
1 Introduction
Shawky and Bakoban 2008 discussed the exponentiated gamma distribution as an important model of life time models and derived Bayesian and nonBayesian estimators of the shape parameter, reliability and failure rate functions in the case of complete and typeII censored samples. Also order statistics from exponentiated gamma distribution and associated inference was discussed by Shawky and Bakoban 2009. Ghanizadeh, et al. 2011, dealt with the estimation of parameters of the exponentiated gamma (EG) distribution with presence of k outliers. The maximum likelihood and moment estimators were derived. These estimators are compared empirically using Monte Carlo simulation. Singh et al. 2011b proposed Bayes estimators of the parameter of the exponentiated gamma distribution and associated reliability function under general entropy loss function for a censored sample. The proposed estimators were compared with the corresponding Bayes estimators obtained under squared error loss function and maximum likelihood estimators through their simulated risks. Khan and Kumar 2011established the explicit expressions and some recurrence relations for single and product moments of lower generalized order statistics from exponentiated gamma distribution. Sing et al. 2011a where proposed Bayes estimators of the parameter of the exponentiated gamma distribution and associated reliability function under general entropy loss function for a censored sample. Feroze ans Aslam 2012 introduced Bayesian analysis of exponentiated gamma distribution under type II censored samples. Recently, Nasiri et al. 2013 discussed Classical and Bayesian estimation of parameters on the generalized exponentiated gamma distribution.
2 McDonald generalized distribution
is the wellknown hypergeometric functions which are well established in the literature (see, Gradshteyn and Ryzhik 2000). Some mathematical properties of the cdf F(x) for any McG distribution defined from a parent G(x) in Equation 5, could, in principle, follow from the properties of the hypergeometric function, which are well established in the literature (Gradshteyn and Ryzhik 2000 Sec. 9.1). One important benefit of this class is its ability to skewed data that cannot properly be fitted by many other existing distributions. Mc G family of densities allows for higher levels of flexibility of its tails and has a lot of applications in various fields including economics, finance, reliability, engineering, biology and medicine.
respectively. Recently Cordeiro et al. 2012 presented results on the McDonald normal distribution. Cordeiro et al. 2012 proposed McDonald Weibull distribution, Merovci and Elbatal 2013 proposed McDonald modified Weibull distribution, Elbatal et al. 2014 proposed McDonald generalized linear failure rate Distribution, Elbatal and Merovci 2014 introduced McDonald Pareto distribution and Marciano et al. 2012 obtained the statistical properties of the Mc  Γ and applied the model to reliability data. In this paper we introduce a new class of distribution, called McDonald exponentiated gamma (McEG) distribution which extends the exponentiated gamma model and has several other models as special cases. since it has more shape parameters, yielding a large variety of forms. It can also be useful for testing the goodness of fit of its submodels.
The outline of this paper is as follows. In Section 2, the McDonald exponentiated gamma (MceG) and related family distributions are introduced. The series expansion for the density, hazard and reverse hazard functions, and other properties are presented in Section 3. Section 4 provides expansions for the cumulative and density functions. In Section 5, we present the statistical properties, in particular moments, moment generating function. The distribution of the order statistics is expressed in Section 6. Section 7 provides least squares and weighted least squares estimators. Maximum likelihood estimates of the parameters index to the distribution are discussed in Section 8. Section 9 provides applications to real data sets. Section 10 ends with some conclusions.
3 McDonald exponentiated gamma distribution
where
respectively.
4 Expansions for the cumulative and density functions
for b > 0 is an integer. Where are constants such that and G(x,λ,θc(a + j)) is a finite mixture of exponentiated gamma distribution with λ and θc(a + j) are scale and shape parameters respectively. The graphs below are the pdf, cdf, survival function, h(x), and τ(x) of the McEG distribution for different values of parameters λ,θ,a,b and c.
5 Statistical properties
This section is devoted to studying statistical properties of the (McEG) distribution, specifically quantile function, moments and moment generating function
5.1 Quantile function and simulation
5.2 Moments
In this subsection we discuss the r_{ th } moment for (McEG) distribution. Moments are necessary and important in any statistical analysis, especially in applications. It can be used to study the most important features and characteristics of a distribution (e.g., tendency, dispersion, skewness and kurtosis). We use the results presented earlier, which was obtained by expanding the pdf.
5.3 Moment generating function
In this subsection we derived the moment generating function of (McEG) distribution.
which completes the proof.
6 Conditional moments, residual life and reversed failure rate function
Using m(t)and m_{2}(t) we obtain the variance of the reversed residual life of the McEG distribution, and hence the coefficient of variation of the reversed residual life of the McEG distribution can be easily obtained.
7 Distribution of the order statistics
In this section, we derive closed form expressions for the pdfs of the r_{ th } order statistic of the (McEG) distribution, also, the measures of skewness and kurtosis of the distribution of the r_{ th } order statistic in a sample of size n for different choices of n;r are presented in this section. Let X_{1},X_{2},…,X_{ n } be a simple random sample from (McEG) distribution with pdf and cdf given by (7) and (9), respectively.
substituting from (7) and (8) into (37), we can express the k_{ th } ordinary moment of the r_{ th } order statistics X_{r:n} say as a liner combination of the k_{ th } moments of the (McEG) distribution with different shape parameters. Therefore, the measures of skewness and kurtosis of the distribution of X_{r:n} can be calculated.
8 Estimation and inference
The elements of Hessian matrix is given in the Appendix.
where z_{ γ } is the upper 100γ_{ the } percentile of the standard normal distribution.
We can compute the maximized unrestricted and restricted loglikelihood functions to construct the likelihood ratio (LR) test statistic for testing on some the McEG submodels. For example, we can use the LR test statistic to check whether the McEG distribution for a given data set is statistically superior to the EG distribution. In any case, hypothesis tests of the type H_{0}:φ = φ_{0} versus H_{0}:φ ≠ φ_{0} can be performed using a LR test. In this case, the LR test statistic for testing H_{0} versus H_{1} is , where and are the MLEs under H_{1} and H_{0}, respectively. The statistic ω is asymptotically (as n → ∞) distributed as , where k is the length of the parameter vector θ of interest. The LR test rejects H_{0} if , where denotes the upper 100γ% quantile of the distribution.
9 Application
The LR test statistic to test the hypotheses H_{0}:a = b = c = 1 versus H_{1}:a ≠ 1 ∨ b ≠ 1 ∨ c ≠ 1 is , so we reject the null hypothesis.
where k is the number of parameters in the statistical model, n the sample size and ℓ is the maximized value of the loglikelihood function under the considered model. Also, here for calculating the values of KS we use the sample estimates of θ,α,a,b and c. Table 1 shows the MLEs under both distributions, Table 2 shows the values of 2ℓ, AIC and CAIC values. The values in Table 2 indicate that the McEG distribution leads to a better fit than the EG distribution.
A density plot compares the fitted densities of the models with the empirical histogram of the observed data (Figure 5). The fitted density for the McEG model is closer to the empirical histogram than the fits of the EG model.
Estimated parameters of the EG and McEG distribution for the data set
Model  Parameter Estimate(St. Err)  ℓ(·;x) 

EG 
 30.080 
KEG 
 15.962 
 
McEG 
 14.852 

Criteria for comparison
Model  KS  2ℓ  AIC  CAIC 

EG  0.211  60.161  64.161  64.361 
KEG  0.146  31.925  39.925  40.615 
McEG  0.139  29.704  39.704  40.578 
10 Simulated data
 1.
Set n, and Θ = (λ,θ,a,b,c).
 2.
Set initial value x^{0} for the random starting.
 3.
Set j = 1.
 4.
Generate U ∼ Uniform (0,1).
 5.
Update x^{0} by using the Newton’s formula such as
 6.
If ∣x^{0}x^{⋆}∣ ≤ ε, (very small, ε > 0 tolerance limit). Then, x^{⋆} will be the desired sample from F(x).
 7.
If ∣x^{0}x^{⋆}∣ > ε, then, set x^{0} = x^{⋆} and go to step 5.
 8.
Repeat steps 47, for j = 1,2,…,n and obtained x_{1},x_{2},…,x_{ n }.
respectively. The asymptotic confidence intervals for (λ,θ,a,b,c) are obtained as (0 ∼ 0.278), (0 ∼ 13.295), (0 ∼ 0.444), (0 ∼ 8.342) and (0 ∼ 21.30274044) respectively.
11 Conclusion
Here we propose a new model, the socalled the McEG distribution which extends the EG distribution in the analysis of data with real support. An obvious reason for generalizing a standard distribution is because the generalized form provides larger flexibility in modeling real data. We derive expansions for the moments and for the moment generating function. The estimation of parameters is approached by the method of maximum likelihood, also the information matrix is derived. We consider the likelihood ratio statistic to compare the model with its baseline model. An application of the McEG distribution to real data show that the new distribution can be used quite effectively to provide better fits than EG distribution.
Appendix
Declarations
Acknowledgements
This project was supported by King Saud University, Deanship of Scientific Research, College of Sciences Research Center.
Authors’ Affiliations
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