An extendedG geometric family
 Gauss M. Cordeiro^{1},
 Giovana O. Silva^{2}Email author and
 Edwin M. M. Ortega^{3}
https://doi.org/10.1186/s4048801600414
© Cordeiro et al. 2016
Received: 31 July 2015
Accepted: 3 February 2016
Published: 16 February 2016
Abstract
We introduce and study the extendedG geometric family of distributions, which contains as special models some important distributions such as the XTG (Xie et al. 2002) geometric, Weibull geometric, Chen (Chen 2000) geometric, Gompertz geometric, among others. This family not only includes distributions with bathtub and unimodal failure rate functions but provides a broader class of monotone failure rates. Its density function can be expressed as a linear mixture of extendedG densities. We derive explicit expansions for the ordinary and incomplete moments, generating function, mean deviations and Rénvy entropy. The density of the order statistics can also be given as a linear mixture of extendedG densities. The model parameters are estimated by maximum likelihood. The potentiality of the new family is illustrated by means of an application to real data.
MSC
60E05, 62P10, 62P30
Keywords
Expected information ExtendedG distribution Geometric distribution Lifetime distribution Maximum likelihood estimationIntroduction
In the last few years, new classes of distributions were proposed by extending the Weibull distribution to cope with bathtub shaped failure rates. A good review of some of these models is addressed by Pham and Lai (2007). Among these models, we point out the exponentiated Weibull (Mudholkar et al. 1995, 1996), additive Weibull (AW) (Xie and Lai 1995), XTG (Xie et al. 2002), modified Weibull (MW) (Lai et al. 2003), beta exponential (Nadarajah and Kotz 2006), BLZ (Bebbington et al. 2007), generalized modified Weibull (GMW) (Carrasco et al. 2008), beta modified Weibull (BMW) (Silva et al. 2010a) and beta Weibull geometric (Cordeiro et al. 2013) distributions.
Alternatively, various works introduced more flexible distributions in modeling monotone or unimodal failure rates but it is not useful for modeling the bathtub shaped failure rates. Adamidis and Loukas (1998) defined the exponential geometric distribution to model lifetime data with decreasing hazard rate function (hrf). Gupta and Kundu 1999, 2001a, b proposed the generalized exponential (GE) (also called the exponentiated exponential) distribution, and investigated some of its mathematical properties. This distribution has only increasing or decreasing hrf. Following the same idea of the GE distribution, Silva et al. (2010b) proposed the generalized exponential geometric (GEG) model and demonstrated that its hrf can be increasing, decreasing or unimodal. Another generalization of the Weibull for modeling monotone or unimodal failure rates, referred to as the Weibull geometric (WG) distribution, was investigated by BarretoSouza et al. (2011). The Kumaraswamy loglogistic distribution was studied by de Santana et al. (2012). This generalization contains distributions with unimodal and bathtub shaped hrfs.
In this paper, we propose the new extendedG geometric (EGG) family of lifetime models by compounding the extendedG (EG) (Nadarajah and Kotz 2005) class of models and the geometric distribution. This family has two important aspects: it involves one additional shape parameter to the baseline model and the new parameter has a clear physical interpretation. We expect that it will attract wider applications in biology, medicine and reliability, and other areas of research.
Further, several distributions are obtained as special cases of this family including the extended exponential geometric (EEG), modified Weibull geometric (MWG), exponential power geometric (EPG), logWeibull geometric (LWG), generalized power Weibull geometric (GPWG), among several others. Besides these distributions, the EGG family contains other promising new distributions as, for example, the additive Weibull geometric (AWG) and XTG geometric (XTGG) distributions (see “Appendix: new special EGG models”). Due to its flexibility in accommodating different forms of the risk function, the new family is an important tool to be used in a variety of problems in modeling survival data. Various EGG distributions are not only convenient for modeling comfortable bathtubshaped failure rates but they are also suitable for testing goodnessoffit of some special models.
The paper is organized as follows. In Section 2, we define the EGG family and demonstrate that its probability density function (pdf) is given as a linear mixture of EG densities. In Sections 3 and 4, we derive the moments and moment generating function (mgf), respectively. The mean deviations and Bonferroni and Lorenz curves are obtained in Section 5. In Section 6, we demonstrate that the Rényi entropy of the EGG family is a linear combination of EG entropies with different scale parameters. In Section 7, the density function of the EGG order statistics is expressed as a linear mixture of EG densities. Maximum likelihood estimation of the model parameters is addressed in Section 8. In Section 9, we give an application to ozone level data to prove empirically the potentiality of the proposed family. Concluding remarks are provided in Section 10. Some special models are presented in the “Appendix: new special EGG models”.
The EGG family
where h(y)=∂ H(y)/∂ y. We denote by Y _{ α }∼EG(α,τ) a random variable Y _{ α } having density function (2). The MW distribution is a special case of (2) when \(H(y)=y^{\gamma }\exp (\lambda y)\), where γ>0 and λ≥0. Clearly, the Weibull distribution is obtained as a basic exemplar when λ=0.
Inverse function x=H ^{−1}(y) for some EG models
Distribution  x=H ^{−1}(y) 

Exponential power  \(\frac {[\log (y+1)]^{1/\beta }}{\lambda }\) 
Chen  \(\left [\log (y+1)\right ]^{1/\beta }\) 
XTG  \(\lambda \big [\log \big (y/\lambda +1\big)\big ]^{\frac {1}{\beta }}\) 
LogWeibull  \(\sigma \log (y)+\mu \) 
Kies  \(\frac {y^{1/\beta }\sigma +\mu }{y^{1/\beta }+1}\) 
Generalized power Weibull  \(\beta \left [(y+1)^{1/\theta }1\right ]^{1/\alpha _{1}}\) 
BLZ  \(\frac {\log (t)\pm \sqrt {\left [\log (y)\right ]^{2}+4\alpha _{1}\beta }}{2\alpha _{1}}\) 
Gompertz  \(\frac {\log \left (\alpha _{1}y+1\right)}{\alpha _{1}}\) 
Pham  \(\Big [\frac {\log (1+y)}{\log (a_{1})}\Big ]^{1/\alpha _{1}}\) 

Reliability  the new distribution can arise in series and parallel systems with identical components, which appear in many industrial applications and biological organisms;

Times to the first and last failure  suppose the failure of a device occurs due to the presence of an unknown number Z of initial defects of same kind, which can be identifiable only after causing failure and are repaired perfectly. Let Y _{ i } be the time to the failure of the device due to the ith defect, for i≥1. Under the assumptions that the Y _{ i }’s are iid EG random variables independent of Z, the EGG distribution is appropriate for modeling the times to the first and last failures, respectively;

Time to relapse of cancer under the firstactivation scheme  suppose that an individual in the population is susceptible to a certain type of cancer. Let Z be the number of carcinogenic cells for that individual left active after the initial treatment and denote by Y _{ i } the time spent for the ith carcinogenic cell to produce a detectable cancer mass, for i≥1. Under the assumptions that \(\left \{Y_{i}\right \}_{i\geq 1}\) is a sequence of iid EG random variables independent of Z, we conclude that the time to relapse of cancer of a susceptible individual has the EGG distribution.
The MWG distribution is obtained from (4) when \(H(x)=x^{\gamma }\exp (\lambda x)\), where γ>0 and λ≥0. Further, the WG distribution is also obtained as a special case when λ=0. The EG distribution follows as the limiting distribution (the limit is defined in terms of the convergence in distribution) of the EGG distribution when \(p\rightarrow 0^{+}\). On the other hand, if \(p\rightarrow 1^{}\), we obtain the distribution of a random variable Y such that P(Y=0)=1.
where w _{ j }=(1−p) p ^{ j } for j=0,1,… and g _{(j+1)α,τ }(x) denotes the density function of the random variable Y _{(j+1)α }. Evidently, \(\sum _{j=0}^{\infty } w_{j}=1\). Note that the righthand side of (8) is mathematically more tractable than the density of X. So, the EGG density function can be expressed as a linear mixture of EG densities. Equation (8) is very useful to derive some mathematical quantities (for example, ordinary, incomplete and factorial moments and mgf) for the EGG family from an infinite weighted linear combination of those EG quantities.
Moments
Equation (9) reveals that the moments of X are linear combinations of the corresponding moments of Y _{(j+1)α } for j≥0. So, the moments of the EGG family are expressed as infinite linear combinations of those EG moments.
Several tables provide Mellin transforms for common functions. The Laplace and Mellin transforms are defined in Prudnikov et al. (1986). Equations (9)–(11) are the main results of this section. They can be used to obtain analytically or numerically the moments of several EGG special models.
where \(\Gamma (t,\alpha)=\int _{t}^{\infty }\,w^{\alpha 1}\,\mathrm {e}^{w}dw\) is the upper incomplete gamma function. Equation (12) gives the moments of the XTG distribution. Hence, we can express the XTGG moments by combining Eqs. (9) and (12).
Both integrals in (13) can be evaluated numerically for most EGG distributions.
Generating function
Equations (14) and (15) are the main results of this section. Combining these two equations, we can derive the generating functions of some EGG special models.
Mean deviations
where \(G_{\alpha,{\boldsymbol {\tau }}}(z)=1\exp \big \{\exp \big (\frac {t\mu }{\sigma }\big)\big \}\).
Rényi entropy
where I _{ j }(γ) denotes the EG Rényi entropy with scale parameter \(\alpha _{j}^{\star }\). Hence, the EGG Rényi entropy is a linear combination of EG Rényi entropies with scale parameters given by \(\alpha _{j}^{\star }\) for j=0,1,… The EG entropy depends on the form of H(·) and can be evaluated (at least numerically) using most statistical software programs.
Order statistics
Maximum likelihood estimation
where \(\dot {h}(x_{i})_{{\boldsymbol {\tau }}}=\partial h(x_{i})/\partial {\boldsymbol {\tau }}\) and \(\dot {h}(x_{i})_{{\boldsymbol {\tau }}}=\partial h(x_{i})/\partial {\boldsymbol {\tau }}\) are p×1 vectors. Because the equations U _{ p }(θ)=0, U _{ α }(θ)=0 and U _{ τ }(θ)=0 are nonlinear, the MLE \(\widehat {\boldsymbol {\theta }}\) have to be evaluated numerically. The loglikelihood can be maximized either directly by using the MaxBFGS routine in the matrix programming language Ox (see, Doornik 2007).
For interval estimation and hypothesis tests on the model parameters, we require the observed information matrix J=J(θ), whose elements can be evaluated numerically. Under general regularity conditions, the asymptotic distribution of \((\widehat {\boldsymbol {\theta }}{\boldsymbol {\theta }})\,\ \text {is}\,\ N_{p+2}\left (0,I({\boldsymbol {\theta }})^{1}\right)\), where I(θ) is the expected information matrix. We can substitute I(θ) by the observed information matrix \(J(\widehat {\boldsymbol {\theta }})\) evaluated at \(\widehat {\boldsymbol {\theta }}\). The multivariate normal \(N_{p+2}\left (0,J(\widehat {\boldsymbol {\theta }})^{1}\right)\) distribution can be used to obtain approximate confidence intervals for the individual parameters.
We apply formal goodnessoffit tests in order to verify which distribution gives the best fit. We consider the CramérVon Mises (W ^{∗}) and AndersonDarling (A ^{∗}) statistics. In general, the smaller the values of these statistics, the better the fit to the data. Let F(x;θ) be the cdf, where the form of F is known but θ (a kdimensional parameter vector, say) is unknown. To obtain the W ^{∗} and A ^{∗} statistics, we can proceed as follows: (i) compute \(v_{i} = F\left (x_{i};\widehat {{\boldsymbol {\theta }}}\right)\), where the x _{ i }’s are in ascending order, \(y_{i}=\Phi ^{1}\left (v_{i}\right)\) is the standard normal qf and \(u_{i} = \Phi \{(y_{i}  \overline {y})/s_{y}\}\), where \(\overline {y} = n^{1}\sum _{i=1}^{n}y_{i}\) and \({s^{2}_{y}} = (n1)^{1}\sum _{i=1}^{n}(y_{i}  \overline {y})^{2}\); (ii) calculate \(W^{2} = \sum _{i=1}^{n}\{u_{i}  (2i  1)/(2n)\}^{2} + 1/(12n)\) and \(A^{2} = n n^{1}\sum _{i=1}^{n}\{(2i1)\log (u_{i}) + (2n+12i)\log (1u_{i})\}\) and (iii) modify W ^{2} into W ^{∗}=W ^{2}(1+0.5/n) and A ^{∗} into \(A^{*} = A^{2}\left (1+0.75/n + 2.25/n^{2}\right)\). For further details, the reader is referred to Chen and Balakrishnan (1995).
Application: ozone level data
We illustrate the superiority of some EGG distributions over their submodels. We consider the data set from the New York State Department of Conservation corresponding to the daily ozone level measurements in New York in MaySeptember, 1973. The numerical evaluations are performed using the MaxBFGS subroutine of the Ox program (Doornik 2007).
We consider the reparametrizations, \(\alpha =\alpha _{1}^{\gamma }\) for the MWG model, \(\alpha =\alpha _{1}^{\beta }\) for the XTGG model and \(\alpha _{1}=\alpha _{3}^{\beta _{1}}\) and \(\alpha _{2}=\alpha _{4}^{\beta _{2}}\) for the AWG model. The density functions of the XTGG and MWG models are presented in the “Appendix: new special EGG models”.

BXIIGI distribution$$\begin{array}{@{}rcl@{}} f(x;s,k,c,p)=\frac{(1p)c\,k\,s^{c}\,x^{c1}\,\left[1+\left(\frac{x}{s}\right)^{c}\right]^{k1}} {{\left\{1p\left[1+\left(\frac{x}{s}\right)^{c}\right]^{k}\right\}^{2}}},\quad x>0, \end{array} $$(22)
where s>0 is a scale parameter and k>0, c>0 and p∈(0,1) are shape parameters.

BXIIGII distribution$$\begin{array}{@{}rcl@{}} f(x;s,k,c,p)=\frac{(1p)c\,k\,s^{c}\,x^{c1}\,\left[1+\left(\frac{x}{s}\right)^{c}\right]^{k1}} {{\left\{1p+p\left[1+\left(\frac{x}{s}\right)^{c}\right]^{k}\right\}^{2}}} \quad x>0. \end{array} $$(23)
MLEs of the model parameters for the daily ozone level data, the corresponding SEs (given in parentheses) and the AIC statistics
Model  p  α _{1}  λ  γ  AIC  

MWG  0.83032  104.81  0.0030932  1.6764  1089.21  
(0.18959)  (78.852)  (0.0068905)  (0.23498)  
MW  −  46.080  2.9046e013  1.3402  1091.22  
(3.3755)  (0.00053284)  (0.095420)  
p  α _{1}  λ  β  AIC  
XTGG  0.92198  19.384  119.79  1.7396  −  1088.39 
(0.16726)  (28.978)  (92.031)  (0.19738)  
XTG  −  3.7020  20682.  1.3401  −  1091.22 
(1.8864)  (85.071)  (0.095512)  
p  α _{3}  β _{1}  α _{4}  β _{2}  AIC  
AWG  0.75545  0.99528  116.54  0.99528  116.54  1091.45 
(0.17410)  (0.0393)  (982.7309)  (0.0393)  (0.0393)  
AW  −  0.99622  77.29  0.99622  77.29  1093.22 
(0.0251070)  (512.43)  (0.025107)  (512.43)  
p  s  c  k  AIC  
BXIIGI  0.7553  16102  1.7363  10405  −  1089.5 
(0.1760)  (4316.05)  (0.2089)  (3936.62)  
BXIIGII  1E8  134.00  1.4860  5.5678  −  1092.7 
(1E9)  (17.35)  (0.2235)  (2.4753) 
Formal tests for some models
Model  Statistics  

W ^{∗}  A ^{∗}  
MWG  0.091  0.545 
MW  0.170  0.966 
XTGG  0.078  0.474 
XTG  0.170  0.966 
AWG  0.088  0.539 
AW  0.170  0.966 
BXIIGI  0.088  0.539 
BXIIGII  0.139  0.820 
Concluding remarks
In this paper, we introduce and study a new class of distributions called the extendedG geometric (EGG) family that generalizes the Weibull geometric and modified Weibull geometric distributions proposed by BarretoSouza et al. (2011) and Wang and Elbatal (2015), respectively, among other distributions. This is achieved by compounding the class of extendedG (EG) distributions (Nadarajah and Kotz 2005) with the geometric distribution. The EGG family is quite flexible in analyzing positive data instead of some other special models.
The density function of the EGG family can be expressed as a linear mixture of EG densities. We provide a mathematical treatment of the new family including explicit expressions for the ordinary and incomplete moments, generating function, mean deviations, Bonferroni and Lorenz curves, order statistics and their moments. The estimation of the parameters is approached by the method of maximum likelihood. An application to real lifetime data indicate that the EGG family could provide better fits than other wellknown lifetime models. We expect that the new family of models will attract wider applications in Statistics.
Appendix: new special EGG models

Modified Weibull geometric distribution
The case \(H(x)=x^{\gamma }\exp (\lambda x)\) and \(h(x)=x^{\gamma 1}\,\exp (\lambda x) (\gamma + \lambda x),\) where γ>0 and λ≥0, in Eq. (4), gives the modified Weibull geometric (MWG) distribution. If \(p\rightarrow 0^{+}\), it reduces to the MW distribution. The MWG distribution is very flexible to accommodate a hrf that has increasing, decreasing, bathtub and unimodal shapes. It is also suitable for testing goodness of fit of some special models such as the modified Weibull (MW), Weibull geometric (WG) and Weibull distributions.

Exponential power geometric distribution
For the case \(H(x)=\exp \big [(\lambda x)^{\beta }\big ]1\), \(h(x)=\beta \lambda \exp \big [(\lambda x)^{\beta }\big ] (\lambda x)^{\beta 1}\) and α=1, where β,λ>0, we obtain the exponential power geometric (EPG) distribution. If \(p\rightarrow 0^{+}\) in addition to α=1, it becomes the exponential power (EP) distribution (Smith and Bain 1975). This distribution has the property that its hrf may take a Ushaped form. Smith and Bain (1975) presented some general properties of least squares estimators and discussed the case of locationscale parameter distributions.

Chen geometric distribution
The case \(H(x)=\exp \left (x^{\beta }\right)1\), \(h(x)=\beta x^{\beta 1}\exp \left (x^{\beta }\right)\), where β>0, corresponds to the Chen geometric (CG) distribution. If \(p\rightarrow 0^{+}\), it becomes the Chen distribution (Chen, 2000), which has increasing or bathtubshaped hrf. Chen (2000) discussed exact confidence intervals and exact joint confidence regions for the parameters based on typeII censored samples.

XTG geometric distribution
For \(H(x)=\lambda \big [\exp \big \{\big (x/\lambda \big)^{\beta }\big \}1\big ]\) and \(h(x)=\beta \exp \big \{\big (x/\lambda \big)^{\beta }\big \} (x/\lambda)^{\beta 1}\), where β>0 and λ>0, we obtain the XTG geometric (XTGG) distribution. If \(p\rightarrow 0^{+}\), it reduces to Xie et al.’s (2002) model. They studied parameter estimation methods and showed its applicability. If λ=1, this model reduces to the CG distribution above.

LogWeibull geometric distribution
For \(H(x)=\exp \big [(x\mu)/\sigma \big ]\), \(h(x)=(1/\sigma)\exp \big [(x\mu)/\sigma \big ]\) and α=1, where \(\infty <\mu <\infty \) and σ>0, we have the logWeibull geometric (LWG) distribution. If \(p\rightarrow 0^{+}\), it gives as special case the logWeibull (LW) distribution (White 1969; Lawless 2003). White (1969) obtained the means and variances of the order statistics of the LW distribution and listed these values in special tables. Examples of these tables to obtain weighted least squares estimates from censored samples from a Weibull distribution are also presented. The LW distribution is a very popular distribution for modeling lifetime data and phenomenon with monotone failure rates.

Kies geometric distribution
For H(x)=[(x−μ)/(σ−x)]^{ β } and h(x)=β[(x−μ)/(σ−x)]^{ β−1}[(σ−μ)/(σ−x)^{2}], where \(0<\mu <t<\sigma <\infty \), we obtain the Kies geometric (KG) distribution. If \(p\rightarrow 0^{+}\), it yields as a special model the Kies distribution (Kies 1958), which is an extension of the Weibull distribution for strength modeling.

Phani geometric distribution
The case \(H(x)=(x\mu)^{\beta _{1}}/(\sigma x)^{\beta _{2}}\phantom {\dot {i}\!}\) and \(\phantom {\dot {i}\!}h(x)=(x\mu)^{\beta _{1}1}(\sigma x)^{(\beta _{2}1)}\big [\beta _{1}(\sigma x) +\beta _{2}(x\mu)\big ]\), where \(0<\mu <t<\sigma <\infty \) and β _{1},β _{2}>0, leads to the Phani geometric (PG) distribution. If \(p\rightarrow 0^{+}\), it reduces to the Phani distribution (Phani 1987). If β _{1}=β _{2}=β, it gives the KG distribution discussed before. Phani (1987) presented statistical justification for modifying the Weibull distribution for the analysis of fibre strength data.

Additive Weibull geometric distribution
For \(H(x)=(x/\beta _{1})^{\alpha _{1}}+(x/\beta _{2})^{\alpha _{2}}\phantom {\dot {i}\!}\), \(\phantom {\dot {i}\!}h(x)=(\alpha _{1}/\beta _{1})(x/\beta _{1})^{\alpha _{1}1} +(\alpha _{2}/\beta _{2})(x/\beta _{2})^{\alpha _{2}1}\phantom {\dot {i}\!}\) and α=1, where α _{1},α _{2},β _{1},β _{2}>0, we obtain the additive Weibull geometric (AWG) distribution. If a=b=1 in addition to α=1, it gives the additive Weibull (AW) distribution (Xie and Lai 1995). This distribution has a bathtubshaped failure rate function. Xie and Lai (1995) studied a simple model based on adding two Weibull survival functions, presented some simplifications of the model and analyzed the graphical estimation technique based on the conventional Weibull plot.

Generalized power Weibull geometric distribution
For \(\phantom {\dot {i}\!}H(x)=\big [1+(x/\beta)^{\alpha _{1}}\big ]^{\theta }1\), \(\phantom {\dot {i}\!}h(x)=(\theta \alpha _{1}/\beta)\big [1+(x/\beta)^{\alpha _{1}}\big ]^{\theta 1}(x/\beta)^{\alpha _{1}}\) and α=1, where α _{1},β>0 and θ≥0, we obtain the generalized power Weibull geometric (GPWG) distribution. If \(p\rightarrow 0^{+}\), it becomes the generalized power Weibull (GPW) distribution (Nikulin and Haghighi 2006). They proposed a chisquared type statistic to test the validity of the GPW family based on the headandneck cancer censored data.

BLZ geometric distribution
The case \(H(x)=\exp (\alpha _{1} x\beta /x)\), \(h(x)=\exp \left (\alpha _{1} x\beta /x\right)\left (\alpha _{1}+\beta x^{2}\right)\) and α=1, where α _{1},β>0, leads to the BLZ geometric (BLZG) distribution. It extends the Bebbington et al.’s (2007) distribution, which corresponds to \(p\rightarrow 0^{+}\) and α=1. Its hrf has four different forms: bathtub shape, increasing, decreasing and upsidedown bathtub. They presented the BLZ distribution as an extension of the Weibull, studied its properties and derived explicit formulas for the turning points of the failure rate function in terms of its parameters.

Gompertz geometric distribution
For \(H(x)=\alpha _{1}^{1}\big [\exp \left (\alpha _{1}x\right)1\big ]\) and \(h(x)=\exp \left (\alpha _{1}x\right)\), where \(\infty <\alpha _{1}<\infty \), we obtain the Gompertz geometric (GG) distribution. If \(p\rightarrow 0^{+}\), it gives the Gompertz distribution (Pham and Lai 2007).

Pham geometric distribution
For \(H(x)=a_{1}^{x^{\alpha _{1}}}1\), \(h(x)=\alpha _{1}x^{\alpha _{1}1}\log \left (a_{1}\right) a_{1}^{x^{\alpha _{1}}}\) and α=1, where α _{1},a _{1}>0, we obtain the Pham geometric (PG) distribution. If \(p\rightarrow 0^{+}\), it yields the Pham distribution (Pham 2002). Pham (2002) proposed this distribution, also referred to as the loglog distribution, and showed that it has a Ushaped hrf.

NadarajahKotz geometric distribution
For \(H(x)=x^{b_{1}}[\exp \left (cx^{d}\right)1]\) and \(h(x)=b_{1}x^{b_{1}1}[\exp \left (cx^{d}\right)1]+cdx^{\left (b_{1}+d1\right)}\exp \left (cx^{d}\right)\), where b _{1},c≥0 and d>0, we obtain the NadarajahKotz geometric (NKG) distribution. If \(p\rightarrow 0^{+}\), it gives the distribution due to Nadarajah and Kotz (2005). They presented some modifications of the Weibull distribution and also discussed some modifications suggested by Gurvich et al. (1997). For b _{1}=0 and c=1, this model reduces to the CG distribution discussed before.

SlymenLachenbruch geometric distribution
For \(H(x)=\exp \big [\alpha _{1}+(\beta /2\theta)\left (x^{\theta }x^{\theta }\right)\big ]\), \(h(x)=(\beta /2x)\exp \big [\alpha _{1}+(\beta /2\theta)\left (x^{\theta }x^{\theta }\right)\big ] \left (x^{\theta }+x^{\theta }\right)\) and α=1, where α _{1},θ>0, we obtain the SlymenLachenbruch geometric (SLG) distribution. If \(p\rightarrow 0^{+}\), it becomes the distribution proposed by Slymen and Lachenbruch (1984). They introduced and studied two classes of distributions within the framework of parametric survival analysis. These classes are derived from a general linear form by specifying a function of the survival function under certain restrictions.
 1.The XTGG density function is given by$$\begin{array}{@{}rcl@{}} f(x)&=& \alpha \beta \exp\left\{\left(x/\lambda\right)^{\beta}\right\} (x/\lambda)^{\beta1} \exp\left\{\alpha \lambda \left[\exp\left\{\left(x/\lambda\right)^{\beta}\right\}1\right]\right\}(1p) \\&&\left[1p\exp\left\{\alpha \lambda \left[\exp\left\{\left(x/\lambda\right)^{\beta}\right\}1\right]\right\}\right]^{2}. \end{array} $$(24)
If X is a random variable with density function (24), we write X∼ XTGG (p,α,λ,β).
 2.The MWG density function is given by$$ f(x)=\frac{\alpha x^{\gamma1}\exp(\lambda x)(\gamma+\lambda x)\exp\left\{\alpha x^{\gamma}\exp(\lambda x)\right\}(1p)} {\left\{1p\left[\exp\left\{\alpha x^{\gamma1}\exp(\lambda x)\right\}\right]\right\}}^{2}. $$(25)
If X is a random variable with density function (25), we write X∼ MWG (p,α,λ,γ).
Declarations
Acknowledgments
The financial support from CNPq is gratefully acknowledged.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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