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Showing 6 results for Yaghoobzadeh
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.
Shahram Yaghoobzadeh Shahrastani,
Volume 12, Issue 1 (9-2018)
Abstract
In this paper, based on generalized order statistics the Bayesian and maximum liklihood estimations of the parameters, the reliability and the hazard functions of Gompertz distribution are investigated. Specializations to Bayesian and maximum liklihood estimators, some lifetime parameters of progressive II censoring and record values are obtained. Also by using two real data sets and simulated data accurations of different estimates of the parameters are compared. Next the Bayesian and maximum liklihood estimates of the Gompertz distribution are compared with Weibull and Lomax distrtibutions.
Ali Shadrokh, Shahram Yaghoobzadeh, Masoud Yarmohammadi,
Volume 12, Issue 1 (9-2018)
Abstract
In this article, with the help of exponentiated-G distribution, we obtain extensions for the Probability density function and Cumulative distribution function, moments and moment generating functions, mean deviation, Racute{e}nyi and Shannon entropies and order Statistics of this family of distributions. We use maximum liklihood method of estimate the parameters and with the help of a real data set, we show the Risti$acute{c}-Balakrishnan-G family of distributions is a proper model for lifetime distribution.
Ali Shadrokh, Shahram Yaghoobzadeh Shahrastani,
Volume 13, Issue 2 (2-2020)
Abstract
In this study, the E-Bayesian and hierarchical Bayesian for stress-strength, when X and Y are two independent Rayleigh distributions with different parameters were estimated based on the LINEX loss function. These methods were compared with each other and with the Bayesian estimator using Monte Carlo simulation and two real data sets.
Shahram Yaghoobzadeh,
Volume 14, Issue 1 (8-2020)
Abstract
In this study, the E-Bayesian estimation of the reliability parameter, R = P(Y < X < Z), when X, Y and Z are three independent inverse Rayleigh distribution with different parameters, were estimated based on ranked set sampling method. To assess the efficiency of the obtained estimates, we compute the average absolute bias and relative efficiency of the derived estimates and compare them with those based on the corresponding simple random sample through Monte Carlo simulations. Also, E-Bayesian estimation of R is compared with its maximum likelihood estimation in each method. Finally, three real data sets are used to analyze the estimation methods.
Shahrastani Shahram Yaghoobzadeh,
Volume 17, Issue 1 (9-2023)
Abstract
In this article, it is assumed that the arrival rate of customers to the queuing system M/M/c has an exponential distribution with parameter $lambda$ and the service rate of customers has an exponential distribution with parameter $mu$ and is independent of the arrive rate. It is also assumed that the system is active until time T. Under this stopping time, maximum likelihood estimation and bayesian estimation under general entropy loss functions and weighted error square, as well as under-informed and uninformed prior distributions, the system traffic intensity parameter M/M/c and system stationarity probability are obtained. Then the obtained estimators are compared by Monte Carlo simulation and a numerical example to determine the most suitable estimator.
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