We reveal and investigate the Topp-Leone (TL) Weibull G (TLWG) family, a new generator of continuous lifespan distributions, as a consequence of this article. However, various of its statistical characteristics have been proposed. Examine the TLWG family, a new source of continuous lifespan distributions. The parameters of maximum likelihood (MLL) estimations are predicted. We can see the significance and versatility of the recommended family of algorithms while using the TLW exponential model as an example of the new recommendation family with the help of two real-world examples.

Recently, Nedjar and Zeghdoudi [6] proposed a new lifetime model called gamma Lindley distribution. Roozegar and Nadarajah [8] introduced some notes around gamma Lindley distribution including only some statistical properties and estimations depending on the same probability density function which proposed by Nedjar and Zeghdoudi [6]. In fact, the model proposed by Nedjar and Zeghdoudi [6] is not a probabilistic model. Further, some of its fundamental properties as well as parameter estimations are incorrect. Hence, all corrections which proposed by Roozegar and Nadarajah [8] are also incorrect. On the other hand, Messaadia and Zeghdoudi [5] proposed only one remark around the parameter space of gamma Lindley distribution which proposed by Nedjar and Zeghdoudi [6], but the mathematical properties and estimations are still wrong for both Nedjar and Zeghdoudi [6] and Roozegar and Nadarajah [8]. Because, Messaadia and Zeghdoudi [5] did not discuss any corrections around quantile function, entropies, estimation methods, simulation and data analysis. In this paper, several corrections including probability density function, quantile function, entropies, estimation the model parameters using the maximum likelihood estimation and moment estimation methods and simulation are discussed in-detail because the previous three papers not make a benefit to the readers, especially in practical field.

In this article we introduce and investigate the weighted Shanker distribution, a weighted version of Shanker's (2015) Shanker distribution. To obtain the form of the weighted Shanker distribution, which is shown as a generalization of the Shanker distribution, a special non-negative weight function is considered. The statistical properties of the weighted Shanker distribution are investigated. We propose a maximum likelihood method for estimating the weighted version's unknown parameter. To observe the pattern of estimates for different sample sizes, a sample generation algorithm and a Monte Carlo simulation study are prepared.

A new method for generating family of distributions was proposed. Some fundamental properties of the new proposed family include the quantile, survival function, hazard rate function, reversed hazard and cumulative hazard rate functions are provided. This family contains several new models as sub models, such as the Weibull exponential model which was defined and discussed its properties. The maximum likelihood method of estimation is using to estimate the model parameters of the new proposed family. The flexibility and the importance of the Weibull-exponential model is assessed by applying it to a real data set and comparing it with other known models.