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Showing 5 results for Geometric Distribution
Shahram Yaghoubzadeh, Ali Shadrokh, Masoud Yarmohammadi,
Volume 9, Issue 1 (9-2015)
Abstract
In this paper, we introduce a new five-parameters distribution with increasing, decreasing, bathtub-shaped failure rate, called as the Beta Weibull-Geometric (BWG) distribution. Using the Sterling Polynomials, the probability density function and several properties of the new distribution such as its reliability and failure rate functions, quantiles and moments, Renyi and Shannon entropies, moments of order statistics, mean residual life, reversed mean residual life are obtained. The maximum likelihood estimation procedure is presented in this paper. Also, we compare the results of fitting this distribution to some of their sub-models, using to a real data set. It is also shown that the BWG distribution fits better to this data set.
Shahram Yaghoobzadeh,
Volume 11, Issue 2 (3-2018)
Abstract
In this paper, the maximum liklihood estimation, unbiased estimations with minimum variance, percentile estimation, best percentile estimation single-observation estimation and the best percentile estimation two-observations in class which are based on order statistics are calculated in two sections for probability density and cumulative distribution functions of the beta Weibull geometric distribution, specially with bathtub-shaped and unimodal failure rate which are useful for modeling of data related to reliability and lifetime. Furthermore, through the simulation method of Monte Carlo and calculation of average square of errors of estimators, they are subjected to comparisons ultimately, the desirable estimator in each section is determined.
Vahid Nekoukhou, Ashkan Khalifeh, Eisa Mahmoudi,
Volume 13, Issue 2 (2-2020)
Abstract
In this paper, we study a three-parameter bivariate distribution obtained by taking Geometric minimum of Rayleigh distributions. Some important properties of this bivariate distribution have been investigated. It is observed that the maximum likelihood estimators of the parameters cannot be obtained in closed forms. We propose to use the EM algorithm to compute the maximum likelihood estimates of the parameters, and it is computationally quite tractable. Based on an extensive simulated study, the effectiveness of the proposed algorithm is confirmed. We also analyze one real data set for illustrative purposes. Finally, we conclude the paper.
Mohammad Hossein Poursaeed, Nader Asadian,
Volume 14, Issue 1 (8-2020)
Abstract
A system in discrete time periods is exposed to a sequence of shocks so that shocks occur randomly and independently in each period with a probability p. Considering k(≥1) as a critical level, we assume that the system does not fail when the number of successive shocks is less than k, the system fails with probability Ө, if the number of successive shocks is equal to k and the system completely fails as soon as the number of sequential shocks reaches k+1. Therefore, this model can be considered as a version of run shock model, in which the shocks occur in discrete periods of time, and the behavior of the system is not fixed when encountering k successive shocks. In this paper, we examine the characteristics of the system according to this model, especially the first and second-order moments of the system's lifetime, and also estimate its unknown parameters. Finally, a method is proposed to calculate the mean of the generalized geometric distribution.
Motahare Zaeamzadeh, Jafar Ahmadi, Bahareh Khatib Astaneh,
Volume 15, Issue 2 (3-2022)
Abstract
In this paper, the lifetime model based on series systems with a random number of components from the family of power series distributions has been considered. First, some basic theoretical results have been obtained, which have been used to optimize the number of components in series systems. The average lifetime of the system, the cost function, and the total time on test have been used as an objective function in optimization. The issue has been investigated in detail when the lifetimes of system components have Weibull distribution, and the number of components has geometric, logarithmic, or zero-truncated Poisson distributions. The results have been given analytically and numerically. Finally, a real data set has been used to illustrate the obtained results.
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