Abstract: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… Show more
“…For more information about simulation study see [10], [11], and [12]. Hence, we assess the performance of the MLE using correct initial values and an obvious algorithm as follows:…”
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.
“…For more information about simulation study see [10], [11], and [12]. Hence, we assess the performance of the MLE using correct initial values and an obvious algorithm as follows:…”
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 2021, Mahmood et al [19] published an enlarged Cosine generalised family of distributions for dependability modelling: characteristics and applications with simulation analysis, and Muse et al [20] suggested a new flexible form of the loglogistic distribution. Citations [5], [17], and [6] in 2022 explored a family of produced distributions with applications.…”
For modelling lifetime data from biological research and engineering, the "Akshaya distribution" is a model one-parameter continuous distribution that was proposed by [15]. The non-Bayesian and Bayesian estimation methods for the Akshaya's parameter are also presented in this study. The weighted least square estimation (WLSE), least square estimation (LSE), Cramer-von-Mises estimation (CVME), and maximum likelihood estimation (MLE), five traditional estimation approaches, are used to find the model parameter. The parameter of the suggested distribution was also determined using the squared error loss function and Bayesian estimating (BE) under independent gamma priors. Finally, a simulation study is used to expound on the applicability and value of the proposed distribution.
“…The recurrence relations (RRs) for moments of DGOS for exponentiated Weibull model have been investigated by [7] whereas the relations for moments of power function model was investigated by [8]. Topp-Leone Weibull generated family of distributions with applications was found in [19]. The RRs for moments of DGOS for a inverted Kumaraswamy model was found in [9].…”
The relationships for moments of dual generalized order statistics (DGOS) for a transmuted exponential (TEx) model are given in this study. These relations are useful when determining the single and product moments of DGOS for a TEx model recursively. Some expressions for recursive computation of moments for special cases of DGOS from a TEx model are given. Some characterizations of the distribution are also given at the end.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.