Some new families of exponentiated Weibull (EW) distribution named T-Exponentiated Weibull{Y} using the T-R{Y} context are presented in this article. The quantile functions of five notable distributions, namely, Logistic, Log-Logistic, Rayleigh, Exponential and Lomax were used to develop five sub-families T-EW{Logistic}, T-EW{Log-Logistic}, T-EW{Rayleigh}, T-EW{Exponential} and T-EW{Lomax} and some general properties such as the quantile functions, Moments, mean deviations from the mean and median, Shannon entropies are obtained. The shapes of the exponentiated Weibull family densities shows can be unimodal, bimodal, monotonically decreasing, skewed to the left, skewed to the right and almost symmetric curves while the hazard function can be bathtub shaped, up-side down bathtub shaped, and increasing-deceasing-increasing. To demonstrate the flexibility and usefulness of the model, three real-life datasets analyzed and the results compared with some competing models.
Weibull distribution is one of the distributions widely studied in statistical literature because of its structural properties and flexibility in modeling lifetime datasets. Some of the areas of application include insurance, metrology and environmental sciences, informatics, forestry, medicine, engineering and reliability analysis, economics and social sciences. In the past few decades, complex nature of lifetime datasets has made researchers to constantly seek for ways of making the existing distributions more flexible and capable of dealing with these emerging complexities. These involves transformation of existing distributions 1, adding skewness to distribution 2, compounding of continuous univariate distributions and discrete distributions 3, 4, and transmutation of distributions, addition of scale, shape or location parameters which help in reflection of the distributions. 5 proposed the exponentiated Weibull distribution by adding extra shape parameter to the Weibull distribution by raising the cumulative distribution function (cdf) to a positive index parameter. The introduction of the new positive index parameter to Weibull distribution extended the distribution and made it more applicable in modeling data in several areas.
Studies of some new generalized families utilizing the T-R{Y} structure include the works of 6, 7, 8, 9, 10, 11. In each case, the quantile function of Y random variable were used to realizing generalizations of the expoenentiated Weibull distributions.
The remaining part of the paper is organized as follows: In section 2, the T-R{Y} structure which forms the backbone of the work, the quantile function and their support and different generalizations of the exponentiated Weibull distributions are given. In section 3, several properties of the T-exponentiated Weibull family of distributions are derived. In Section 4, some new generalized exponentiated Weibull distributions are developed.
Given a random variable X, the 3-parameter Exponentiated Weibull (EW) distribution cumulative distribution function (cdf) and probability density function (pdf) are respectively given as
 | (1) |
and
 | (2) |
where 
is the rate parameter and 
are the shape parameters.
Suppose we have 
and 
random variables with cumulative distribution functions (cdfs) 
and 
respectively and corresponding probability density function (pdfs) 
and 
The quantile functions 
of the T, R and Y random variables are 
and 
respectively. Since T and Y must be on the same support, if 
and 
then 
and 
Utilizing the T-R{Y}structure by 12 and 13, the cumulative distribution function (cdf), the probability density function (pdf) and hazard rate function (hrf) of the T-R{Y} family is presented respectively as
 | (3) |
 | (4) |
and
 | (5) |
For any non-uniform T and Y random variables, and given the random variable R, T-R{Y} generalizes the R distribution.
Five different families of generalized exponentiated Weibull (GEW) distribution is provided using the quantile function of the Y random variable.
Following equations (3), (4) and (5), and using the quantile functions in Table 1, the cdf, pdf and hrf of each of the family of distributions; (i) T-exponentiated Weibull{Logistic}, (ii) T-exponentiated Weibull{Log-Logistic}, (iii) T-exponentiated Weibull{Rayleigh}, (vi) T-exponentiated Weibull{Exponential} and (v) T-exponentiated Weibull{Lomax}are given by
i) T-Exponentiated Weibull{Logistic} (T-EW{L})
Suppose the quantile function for Y random variable follows the Logistic distribution, then the distribution function 
density function 
and hazard function 
of T-EW{L} are presented respectively in (6), (7), (8) below
 | (6) |
 | (7) |
 | (8) |
where 
and 
are the probability density function, cumulative density function and failure rate function of the exponentiated Weibull distribution, and 
is the failure rate function of the 
random variable.
ii) T-Exponentiated Weibull {Log-Logistic} (T-EW{LL})
Assuming the quantile function for Y random variable follows the Log-Logistic distribution, then the distribution function 
density function 
and hazard function 
of T-EW{LL} are presented respectively in (9), (10), (11) below
 | (9) |
 | (10) |
 | (11) |
iii) T-Exponentiated Weibull {Rayleigh} (T-EW{R})
Let the quantile function for the Y random variable follow the Rayleigh distribution, then the distribution function 
density function 
and hazard function 
of T-EW{R} are presented in (12), (13) and (14) below
 | (12) |
 | (13) |
 | (14) |
iv) T-Exponentiated Weibull {Exponential} (T-EW{E})
Suppose the quantile function for Y random variable follows exponential function, then the distribution function 
density function 
and hazard function 
of T-EW{E} are presented in (15), (16) and (17) below
 | (15) |
 | (16) |
where 
is the cumulative hazard function of R random variable. T-EW{Ex} is
 | (17) |
where 
and 
is the survival function and hazard rate function of R random variable respectively.
 | (18) |
v) T-Exponentiated Weibull{Lomax}(T-EW{Lx})
Suppose the quantile function for Y random variable follows Lomax function, then the distribution function 
density function (
) and hazard function (
) of T-EW{Lx} are presented in (19), (20) and (21) below
 | (19) |
 | (20) |
 | (21) |
Here, some general properties of the T-exponentiated Weibull family of distributions are discussed.
Relationships between the Random Variable T, and the Random Variables, X.
Lemma 1 (Transformations).
Given that 
then 
and using the relation 
connection between the T random variable and X random variable can be established by transformation.
i) 
ii) 
iii) 
iv) 
v) 
Lemma 2 (Quantiles). Suppose the random variable X has quantile function 
with 
The quantile functions for the (i) T-exponentiated Weibull {Logistic}, (ii) T-exponentiated Weibull{Log-Logistic}, (iii) T-exponentiated Weibull {Rayliegh}, (iv) T-exponentiated Weibull{Exponential} and (v) T-exponentiated Weibull{Lomax} distributions are respectively given as
i) 
ii) 
iii) 
iv) 
v) 
Proof: By solving for 
directly from 
the results follow.
Theorem 1: The Shannon entropies of the T-Exponentiated Weibull {Y} family of distributions for (i) T-exponentiated Weibull{Logistic}, (ii) T-exponentiated Weibull{Log-Logistic}, (iii) T-exponentiated Weibull{Rayleigh}, (iv) T-exponentiated Weibull{Pareto}, (v) T-exponentiated Weibull{Exponential} and (vi) T-exponentiated Weibull{Lomax}
 | (22) |
 | (23) |
 | (24) |
 | (25) |
 | (26) |
where 
denotes the Shannon entropy of the T random variable.
Proof: The prove of the result in equation (22) for the T-exponentiated Weibull {Logistic}family shall be provided here. Given the expression for the Shannon entropy of the random variable X as
 |
But, 
and 
Given the random variable 
for the T-exponentiated Weibull {Logistic} family, then
 | (27) |
Also,
 |
 |
It follows from Lemma1 (i), that 
follows the T-exponentiated Weibull{Logistic} family, thus
 | (28) |
Result from equation (22) follows from equations (27) and (28). Following the same approach results in equations (23) – (26) are obtained.
Theorem 2: The 
non-central moments for the (i) T-exponentiated Weibull {Logistic}, (ii) T-exponentiated Weibull{Log-Logistic}, (iii) T-exponentiated Weibull {Rayliegh} and (iv) T-exponentiated Weibull {Pareto}, (v) T-exponentiated Weibull {exponential} and (vi) T-exponentiated Weibull{Lomax} distributions are respectively given by
 | (29) |
 | (30) |
 | (31) |
 | (32) |
 | (33) |
Here, 
is the moment generation function of the random variable X.
Deviation from the Mean D(µ) and from the Median D(M)
The absolute mean deviation from the Mean and absolute mean deviation from the median for (i) T-exponentiated Weibull{Logistic}, (ii) T-exponentiated Weibull{Log-Logistic} (iii) T-exponentiated Weibull{Rayliegh}, (iv) T-exponentiated Weibull{Pareto}, (v) T-exponentiated Weibull{exponential} and T-exponentiated Weibull{Lomax} distributions, are given respectively as
 | (34) |
 | (35) |
 |
 |
 |
 |
Proof: Using the deviation from the mean and deviation from the median for the T-exponentiated Weibull{Logistic} distribution. Suppose the expression for the mean deviation from the mean 
and from the median 
are given as is given by
 |
and 
respectively and
 |
Then
 |
Taking 
then
 |
 |
 |
But 
then
 |
 |
Therefore,
 |
Using logarithmic series representation (see formula 1.511 in 14) 
we have that
 |
 |
Substituting, we have
 |
Which can be simplified as
 |
Some new generalized Exponentiated Weibull distributions in the T-Exponentiated Weibull {Y} families are presented in this section. The additional parameter(s) from the T and Y usually deepens the flexibility of the distributions and also improves the tail behaviour. The five distributions are Gumbel-Exponentiated Weibull {Logistic} (Gu-EW{L}) 15, Frechet-Exponentiated Weibull {Log-Logistic} (F-EW{LL}), Chen-exponentiated Weibull{Rayleigh} (Chen-EW{R}), Burr(III)-Exponentiated Weibull{Exponential} (Burr(III)-EW{E}) and Exponential-Exponentiated Weibull {Lomax} (E-EW{Lx}).
3.1. Gumbel-Exponentiated Weibull{Logistic}(Gu-EW{L}) DistributionLet the random variable T follow the Gumbel distribution with location parameter 
and scale parameter 
and cdf given by
 |
Then the cdf of the random variable X following the Gu-EW{L} distribution is obtained as
 |
and the pdf of the random variable following the Gu-EW{L} obtained as
 |
where, 
presents the shape parameters, 
is the rate parameter and 
is the location parameter. 
is the cdf of exponentiated Weibull distribution and 
is the pdf of exponentiated Weibull distribution given in equations (1) and (2) respectively. If the shape parameter 
Gu-EW{L} distribution reduces to Gumbel-Weibull (GW) distribution.
The graph shows different shapes of the distribution for different choice of the parameters (skewed to the left, bimodal, skewed to the right and monotonically decreasing). The parameters b and s are from the Gumbel distribution.
3.2. Frėchet-Exponentiated Weibull{Log-Logistic} (F-EW{LL}) DistributionLet the random variable T follow the Frechet distribution with parameter 
cdf of Frechet distribution is given by 
The cdf for the Frechet-Exponentiated Weibull{Log-Logistic}(F-EW{LL}) distribution is given by
 |
 |
where, 
represents shape parameters and λ is the rate parameter. 
is the cdf of exponentiated Weibull distribution and 
is the pdf of exponentiated Weibull distribution given in equations (1) and (2) respectively. The parameter 
is a shape parameter from log-logistic distribution and the parameter 
is shape parameter from the Frėchet distribution.
The plots in Figure 2 give a reversed-J, right-skewed, left-skewed unimodal densities and almost bimodal density shapes.
3.3. Chen-Exponentiated Weibull{Rayleigh} (Chen-EW{R}) DistributionLet T be Chen random variable with distribution function given by 
then the cdf and pdf of the random variable X following the Chen-EW{R} distribution given by
 |
and
 |
respectively.
where, 
and 
represents shape parameters and 
and λ are the rate parameters. 
is the cdf of exponentiated Weibull distribution and 
is the pdf of exponentiated Weibull distribution given in equations (1) and (2) respectively. The parameter 
is from the Rayleigh distribution and the parameters 
are the scale and shape parameters from the Chen distribution respectively.
The plots in Figure 3 shows a J-shaped, monotonically decreasing (reversed -J shaped), density shapes. The pdf displays curves which are highly heterogeneous in the shapes which supports the fact that the six parameter model is suitable for modelling lifetime for several industrial and economic products.
3.4. Burr(III)-Exponentiated Weibull{Exponential} (Burr(III)-EW{E}) DistributionSuppose T random variables are taking from Burr(III) distribution with distribution function given by 
where c and k are shape parameters of Burr(III) distribution. The distribution function of Burr(III)-EW{E} distribution is obtained as
 |
and
 |
 |
where, 
are the shape parameters and 
is the scale parameter. 
is the cdf of exponentiated Weibull distribution and 
is the pdf of exponentiated Weibull distribution given in equations (1) and (2) respectively.
Figure 4 depicts the behavour of the pdf of Burr(III)-EW{E} for different selections of the parameters. The pdf plots shows reversed-J shaped, right-skewed, left-skewed and unimodal densities.
3.5. Exponential-Exponentiated Weibull{Lomax} (E-EW{Lx}) DistributionLet the random variable 
follow the exponential distribution with parameter 1. The pdf of T random variable is 
Then the cdf of exponential-exponentiated Weibull{Lomax} (E-EW{Lx}) distribution is given by
 |
and pdf is provided as
 |
where, 
are the shape parameters and the scale parameters are 
and λ, 
is the cdf of exponentiated Weibull distribution and 
is the pdf of exponentiated Weibull distribution given in equations (1) and (2) respectively.
The E-EW{Lx} densities take various shapes for selected parameter values such as unimodal, reversed-J shaped, right-skewed and left skewed and almost symmetric shapes.
Suppose 
are independent random sample from a Gumbel-exponentiated Weibull{Logistic} distribution, the likelihood function, L, based on observed values 
for the Gu -EW{L} distribution is given by
 |
By maximizing the likelihood function, the maximum likelihood estimate of 
is obtained. The log-likelihood function for Gu-EW{L} distribution is given by
 |
where,
 | (36) |
The maximum likelihood estimates of 
say 
can be derived by taking the partial derivatives of the log-likelihood function and then solving iteratively,
 | (37) |
 | (38) |
 | (39) |
 | (40) |
 | (41) |
A simulation study to investigate the finite sample properties of the Maximum likelihood estimates of the parameters was conducted. We generated 5000 samples of size 
and 700 and the fixed choice of parameter values, 
Random samples are simulated by using the quantile function of the G-EW{L} distribution and Average Estimates (AEs), Root Mean Square Error (RMSE), Average Width (AW) and Coverage Probability were computed to examine the estimation accuracies. It can be observed from the results that as the sample sizes increases the average estimates of the parameters moves rapidly to the true values. In addition, the coverage probabilities tend to the nominal level of 95% as the samples sizes increases for each of the parameters. For expediency, the results for RMSE and AW are presented graphically in Figure 6.
The graphs of RMSE and AW as a function of n in Figure 6, shows clearly that as the sample size increases the values of RMSE decays to zero and AW decreases reasonably well.
Four applications to real data to establish the usefulness of the Gu-EW{L} distribution since the pdf has different shapes and also to demonstrate that the model can be applied in many situations are presented in this section.
The first dataset relates to the symmetric, multimodal and platykurtic Kevlar 49/epoxy strands failure times data (pressure at 70%) with skewness = 0.0938 and excess kurtosis = 0.9154 extracted from 16. We fit the density of the Gumbel-exponentiated Weibull{Logistic} (Gu-EW{L}) distribution using MLE to the data set and compare with three other distributions namely; Exponentiated Weibull distribution (EWD), Gumbel-Weibull distribution (GWD) and beta-Normal distribution (BND). The result of the parameters estimates, log-likelihood, AIC, K-S and p-value.
The above results in Table 2 clearly show that the proposed Gu-EW{L}distribution provided a good fit to the dataset and also performed better than other distribution in fitting the dataset given that the p-value and the K-S statistic is highest and very close to 1. Besides, the p-value of the K-S statistic for all the fitted distributions is greater than the threshold of 0.05 level of significance showing that all the distributions fitted the data sufficiently and that the proposed Gu-EW{L} distribution greatly presented the best fit.
The dataset two represents the height of 100 Australian athletes collected at the Australian Sport Institute. The dataset was reported by 17. The dataset is skewed to the left. The data is unimodal and leptokurtic (Skewness = -0.5514, excess kurtosis = 1.1131).
Four distributions were used to fit the dataset namely: the proposed Gu-EW{L}D, the WD, the EWD, and the BND. The results of the maximum likelihood fit of all the distributions are contained in Table 3.
Results in Table 3 clearly show that the proposed Gu-EW{L}D not only provided a good fit to the data but also performed better the other distribution in fitting the dataset given that its p-value of the K-S statistic is highest. Again, the p-value of the K-S statistic for all the fitted distributions is greater than the nominal 0.05 level of significance indicating that all the distributions fitted the data considerably well and that the proposed Gu-EW{L}D presented the best fit.
Dataset three represents the Kevlar 49/epoxy strands failure times data (pressure at 90%) collected from 18 and obtained from 19. The data set is unimodal, highly skewed to the right and leptokurtic (Skewness = 2.9573, excess kurtosis = 13.3798). Gumbel-exponentiated Weibull {Logistic}(Gu-EW{L}) distribution, Weibull Distribution (WD), exponentiated Weibull distribution (EWD) and beta-Normal distribution (BND) were fitted to the data set and presented below.
From the result of dataset three shown in Table 4, it can be observed that the proposed Gu-EW{L}distribution provided a good fit to the data and also performed better than other competing distributions by providing the highest p-value of the K-S statistic. Again, the p-value and the K-S statistic for all the fitted distributions is greater than the threshold of 0.05 level of significance indicating that all the distributions fitted the data substantially well and that the proposed Gu-EW{L}distribution presented the best fit which can be observed in Figure 9.
This work has introduced a new generalization of the 3-parmater exponentiated Weibull univariate continuous distribution called the T–exponentiated Weibull {Y} distribution. Five new generalized exponentiated Weibull families using the quantile function of Logistic, Log-logistic, Rayleigh, Exponential and Lomax distributions are provided. Several mathematical properties of the new families such as the quantiles, Shannon entropies, non-central moments, absolute mean deviation from the mean and median were obtained.
Five new distributions, Gumbel-Exponentiated Weibull {Logistic} (Gu-EW{L}) (Anyiam and Onyeagu,2021), Frechet - Exponentiated Weibull {Log - Logistic} (F - EW{LL}), Chen - exponentiated Weibull{Rayleigh} (Chen - EW{R}), Burr(III) - exponentiated Weibull{Exponential} (Burr (III) - EW{E}) and Exponential-Exponentiated Weibull {Lomax} (E-EW{Lx}) distributions are presented.
Three real-life datasets are fitted using the Gumbel-Exponentiated Weibull{Logistic} (Gu-EW{L} distribution to exhibit the flexibility and applicability of the proposed generalized exponentiated Weibull family of distributions. The results of the comparison of the T-exponentiated Weibull with other existing distributions showed that the new proposed family performed better when the AICs, K-S statistics and P-values were compared.
The reviewers are highly appreciated for making out time to review this article.
There is no funding for this work.
We declare that there is no conflict of interest.
| [1] | Johnson, N. L. (1949). “Systems of frequency curves generated by method of translation”. Biometrika, 36(1/2): 149-176. | ||
| In article | View Article PubMed | ||
| [2] | Chakraborty, S. and Hazarika, P. J. (2011). A survey of the theoretical developments in univariate skew-normal distribution. Assam Statistical. Review, 25:41-65. | ||
| In article | |||
| [3] | Tahir, M. H. and Cordeiro, G. M. (2016). Compounding of distributions: a survey and new generalized classes. Journal of Statistical Distributions and Applications, 3: 13, 1-35. | ||
| In article | View Article | ||
| [4] | Bhatti, F. A., Hamedani, G. G., Najibi, S. M. and Ahmad, M. (2019). On the Extended Chen Distribution: Development, Properties, Characterizations and Applications. Annals of Data Science. 1-22. | ||
| In article | View Article | ||
| [5] | Mudholkar,G. S. and Srivastava, D. K. (1993). Exponentiated Weibull for analyzing bathtub failure-rate data. IEEE Transactions on Reliability. 42(2), 299-302. | ||
| In article | View Article | ||
| [6] | Alzaatreh, A. Lee, C, Famoye, F. and Ghosh, I. (2016). The Generalized Cauchy family of distributions with Applications. Journal of Statistical Distributions and Applications, Springer, Vol.3(1), 1-16. | ||
| In article | View Article | ||
| [7] | Mahmoud, A., Lee, C. and Famoye, F. (2017). Families of distributions arising from the quantile of generalized lambda distribution. Journal of Statistical Distributions and Applications. 4:25, 1-28. | ||
| In article | View Article | ||
| [8] | Yousof, H. M, Alizadeh, M, Jahanshahi, S. M. A, Ramires, T. G., Ghosh, I. and Hamedani, G. G. (2017). The transmuted Topp-Leone G family of distributions: Theory, characterizations and applications. Journal of Data Science, 15,723-740. | ||
| In article | View Article | ||
| [9] | Hamed, D., Famoye, F. and Lee, C. (2018). On families of generalized Pareto distributions: Properties and Applications. Journal of Data Science, 16(2), 377-396. | ||
| In article | View Article | ||
| [10] | Zubair, M., Alzaatreh, A., Cordeiro, G. M., Tahir, M. H. and Mansoor, M.(2018). On the generalized classes of exponential distribution using T-X family framework. Faculty of Sciences and Mathematics, University of Nis, Serbia, Filomat 32:4, 1259-1272. | ||
| In article | View Article | ||
| [11] | Aldeni, M, Famoye, F. and Lee, C. (2020). A generalized family of lifetime distributions with survival models. Journal of Modern Applied Statistical Methods Vol. 18, No.2 eP2944. | ||
| In article | View Article | ||
| [12] | Aljarrah, M. A., Lee, C. and Famoye, F. (2014). On T-X family of distributions using quantile functions. Journal of Statistical Distributions and Applications, 1(2), 1-17. | ||
| In article | View Article | ||
| [13] | Alzaatreh, A., Lee, C. and Famoye, F. (2014). T-normal family of distributions: A new approach to generalize the normal distribution. Journal of Statistical Distributions and Applications, 1: 16. | ||
| In article | View Article | ||
| [14] | Gradshteyn, I. S. and Ryhzik, I. M. (1965). Tables of integrals, series and products. Academic Press, New York. | ||
| In article | |||
| [15] | Anyiam, K. E. and Onyeagu, S. I. (2021). Gumbel-Exponentiated Weibull{Logistic} lifetime distribution and its applications. Open Journal of Statistics, Vol.11, No.5, 817-837. | ||
| In article | View Article | ||
| [16] | Al-Aqtash, R., Lee, C. and Famoye, F. (2014). Gumbel-Weibull distribution: Properties and application. Journal of Modern Applied Statistical Method, 13, 201-225. | ||
| In article | View Article | ||
| [17] | Cook, R.D. and Weisberg, S. (1994). An Introduction to Regression Graphics. New York: John Wiley & Sons, Inc. | ||
| In article | View Article | ||
| [18] | Andrews, D. F. and Herzberg, A. M. (1985). Data: a collection of problems from many fields for students and research worker. Springer Science and Business Media.. | ||
| In article | |||
| [19] | Osatohanmwen, P., Oyegue, F.O. and Ogbonmwan, S.M. (2019). A New Member from the T-X Family of Distributions: The Gumbel-Burr XII distribution and its Properties. Sankhya A, Vol. 81, pp. 298-322. | ||
| In article | View Article | ||
Published with license by Science and Education Publishing, Copyright © 2021 Anyiam Kizito Ebere, Onyeagu Sydney Iheanyi and Njoku Modestus Onyekachi
This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit
https://creativecommons.org/licenses/by/4.0/
| [1] | Johnson, N. L. (1949). “Systems of frequency curves generated by method of translation”. Biometrika, 36(1/2): 149-176. | ||
| In article | View Article PubMed | ||
| [2] | Chakraborty, S. and Hazarika, P. J. (2011). A survey of the theoretical developments in univariate skew-normal distribution. Assam Statistical. Review, 25:41-65. | ||
| In article | |||
| [3] | Tahir, M. H. and Cordeiro, G. M. (2016). Compounding of distributions: a survey and new generalized classes. Journal of Statistical Distributions and Applications, 3: 13, 1-35. | ||
| In article | View Article | ||
| [4] | Bhatti, F. A., Hamedani, G. G., Najibi, S. M. and Ahmad, M. (2019). On the Extended Chen Distribution: Development, Properties, Characterizations and Applications. Annals of Data Science. 1-22. | ||
| In article | View Article | ||
| [5] | Mudholkar,G. S. and Srivastava, D. K. (1993). Exponentiated Weibull for analyzing bathtub failure-rate data. IEEE Transactions on Reliability. 42(2), 299-302. | ||
| In article | View Article | ||
| [6] | Alzaatreh, A. Lee, C, Famoye, F. and Ghosh, I. (2016). The Generalized Cauchy family of distributions with Applications. Journal of Statistical Distributions and Applications, Springer, Vol.3(1), 1-16. | ||
| In article | View Article | ||
| [7] | Mahmoud, A., Lee, C. and Famoye, F. (2017). Families of distributions arising from the quantile of generalized lambda distribution. Journal of Statistical Distributions and Applications. 4:25, 1-28. | ||
| In article | View Article | ||
| [8] | Yousof, H. M, Alizadeh, M, Jahanshahi, S. M. A, Ramires, T. G., Ghosh, I. and Hamedani, G. G. (2017). The transmuted Topp-Leone G family of distributions: Theory, characterizations and applications. Journal of Data Science, 15,723-740. | ||
| In article | View Article | ||
| [9] | Hamed, D., Famoye, F. and Lee, C. (2018). On families of generalized Pareto distributions: Properties and Applications. Journal of Data Science, 16(2), 377-396. | ||
| In article | View Article | ||
| [10] | Zubair, M., Alzaatreh, A., Cordeiro, G. M., Tahir, M. H. and Mansoor, M.(2018). On the generalized classes of exponential distribution using T-X family framework. Faculty of Sciences and Mathematics, University of Nis, Serbia, Filomat 32:4, 1259-1272. | ||
| In article | View Article | ||
| [11] | Aldeni, M, Famoye, F. and Lee, C. (2020). A generalized family of lifetime distributions with survival models. Journal of Modern Applied Statistical Methods Vol. 18, No.2 eP2944. | ||
| In article | View Article | ||
| [12] | Aljarrah, M. A., Lee, C. and Famoye, F. (2014). On T-X family of distributions using quantile functions. Journal of Statistical Distributions and Applications, 1(2), 1-17. | ||
| In article | View Article | ||
| [13] | Alzaatreh, A., Lee, C. and Famoye, F. (2014). T-normal family of distributions: A new approach to generalize the normal distribution. Journal of Statistical Distributions and Applications, 1: 16. | ||
| In article | View Article | ||
| [14] | Gradshteyn, I. S. and Ryhzik, I. M. (1965). Tables of integrals, series and products. Academic Press, New York. | ||
| In article | |||
| [15] | Anyiam, K. E. and Onyeagu, S. I. (2021). Gumbel-Exponentiated Weibull{Logistic} lifetime distribution and its applications. Open Journal of Statistics, Vol.11, No.5, 817-837. | ||
| In article | View Article | ||
| [16] | Al-Aqtash, R., Lee, C. and Famoye, F. (2014). Gumbel-Weibull distribution: Properties and application. Journal of Modern Applied Statistical Method, 13, 201-225. | ||
| In article | View Article | ||
| [17] | Cook, R.D. and Weisberg, S. (1994). An Introduction to Regression Graphics. New York: John Wiley & Sons, Inc. | ||
| In article | View Article | ||
| [18] | Andrews, D. F. and Herzberg, A. M. (1985). Data: a collection of problems from many fields for students and research worker. Springer Science and Business Media.. | ||
| In article | |||
| [19] | Osatohanmwen, P., Oyegue, F.O. and Ogbonmwan, S.M. (2019). A New Member from the T-X Family of Distributions: The Gumbel-Burr XII distribution and its Properties. Sankhya A, Vol. 81, pp. 298-322. | ||
| In article | View Article | ||
