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Showing 5 results for Stochastic Orders
Shahrokh Hashemi-Bosra, Ebrahim Salehi, Volume 11, Issue 1 (9-2017)
Abstract
The (n-k+1)-out-of-n systems are important types of coherent systems and have many applications in various areas of engineering. In this paper, the general inactivity time of failed components of (n-k+1)-out-of-n system is studied when the system fails at time t>0. First we consider a parallel system including two exchangeable components and then using Farlie-Gumbel-Morgenstern copula, investigate the behavior of mean inactivity time of failed components of the system. In the next part, (n-k+1)-out-of-n systems with exchangeable components are considered and then, some stochastic ordering properties of the general inactivity time of the systems are presented based on one sample or two samples.
Majid Chahkandi, Volume 13, Issue 2 (2-2020)
Abstract
The performance of a system depends not only on its design and operation but also on the servicing and maintenance of the item during its operational lifetime. Thus, the repair and maintenance are important issues in the reliability. In this paper, a repairable k-out-of-n system is considered that starts operating at time 0. If the system fails, then it undergoes minimal repair and begins to operate again. The reliability function, hazard rate function, mean residual life function and some reliability properties of the system are obtained by using the connection between the concepts of minimal repair and record values. Some known stochastic orders are also used to compare the lifetimes and residual lifetimes of two repairable k-out-of-n systems. Finally, based on the given information about the lifetimes of k-out-of-n systems, some prediction intervals for the lifetime of the proposed repairable system are obtained.
Jafar Ahmadi, Fatemeh Hooti, Volume 13, Issue 2 (2-2020)
Abstract
In survival studies, frailty models are used to explain the unobserved heterogeneity hazards. In most cases, they are usually considered as the product of the function of the frailty random variable and baseline hazard rate. Which is useful for right censored data. In this paper, the frailty model is explained as the product of the frailty random variable and baseline reversed hazard rate, which can be used for left censored data. The general reversed hazard rate frailty model is introduced and the distributional properties of the proposed model and lifetime random variables are studied. Some dependency properties between lifetime random variable and frailty random variable are investigated. It is shown that some stochastic orderings preserved from frailty random variables to lifetime variables. Some theorems are used to obtain numerical results. The application of the proposed model is discussed in the analysis of left censored data. The results are used to model lung cancer data.
Abdol Saeed Toomaj, Volume 18, Issue 1 (8-2024)
Abstract
In this paper, the entropy characteristics of the lifetime of coherent systems are investigated using the concept of system signature. The results are based on the assumption that the lifetime distribution of system components is independent and identically distributed. In particular, a formula for calculating the Tsallis entropy of a coherent system's lifetime is presented, which is used to compare systems with the same characteristics. Also, bounds for the lifetime Tsallis entropy of coherent systems are presented. These bounds are especially useful when the system has many components or a complex structure. Finally, a criterion for selecting the preferred system among coherent systems based on the relative Tsallis entropy is presented.
Aqeel Lazam Razzaq, Isaac Almasi, Ghobad Saadat Kia, Volume 18, Issue 2 (2-2025)
Abstract
Adding parameters to a known distribution is a valuable way of constructing flexible families of distributions. In this paper, we introduce a new model, the modified additive hazard rate model, by replacing the additive hazard rate distribution in the general proportional add ratio model. Next, when two sets of random variables follow the modified additive hazard model, we establish stochastic comparisons between the series and parallel systems comprising these components.
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