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Showing 9 results for Yaghoobzadeh
Shahrastani Shahram Yaghoobzadeh,
Volume 21, Issue 2 (3-2017)
Abstract
In this study, E-Bayesian of parameters of two parameter exponential distribution under squared error loss function is obtained. The estimated and the efficiency of the proposed method has been compared with Bayesian estimator using Monte Carlo simulation.
Shahram Yaghoobzadeh Shahrastani Shahram Yaghoobzadeh,
Volume 22, Issue 1 (12-2017)
Abstract
In this paper, a new distribution of the three-parameter lifetime model called the Marshall-Olkin Gompertz is proposed on the basis of the Gompertz distribution. It is a generalization of the Gompertz distribution having decreasing failure rate and can also be increasing and bathtub-shaped depending on its parameters. The probability density function, cumulative distribution function, hazard rate function and some mathematical properties of this model such as, central moments, moments of order statistics, Renyi and Shannon entropies and quantile function are derived. In addition, the maximum likelihood of its parameters method is estimated and this new distribution compared with some Gompertz distribution generalizations by means of a set of real data.
Ali Shadrokh, Shahrastani Shahram Yaghoobzadeh,
Volume 22, Issue 2 (3-2018)
Abstract
In this paper, a new five-parameter so-called Beta-Gompertz Geometric (BGG) distribution is introduced that can have a decreasing, increasing, and bathtub-shaped failure rate function depending on its parameters. Some mathematical properties of the this distribution, such as the density and hazard rate functions, moments, moment generating function, R and Shannon entropy, Bonferroni and Lorenz curves and the mean deavations are provided. We discuss maximum likelihood estimation of the BGG parameters from one observed sample. At the end, in order to show the BGG distribution flexibility, an application using a real data set is presented.
Shahrastani Shahram Yaghoobzadeh Shahrastani,
Volume 23, Issue 2 (3-2019)
Abstract
In this paper, a new estimate of exponential type of auxiliary information to help simple random sampling without replacement of the finite population mean is introduced. This new estimator with a few other estimates using two real data sets are compared with the mean square error.
Ali Shadrokh, Shahrastani Shahram Yaghoobzadeh,
Volume 24, Issue 1 (9-2019)
Abstract
In this study, E-Bayesian and hierarchical Bayesian of parameter of Rayleigh distribution under progressive type-II censoring sampales and the efficiency of the proposed methods has been compared with each and Bayesian estimator using Monte Carlo simulation.
Shahrastani Shahram Yaghoobzadeh,
Volume 24, Issue 1 (9-2019)
Abstract
In this paper, reliability in multi-component stress-strength models, when the stress and strength variables are inverse Rayleigh distributions with different parameters of alpha and beta. Estimates of the maximum likelihood, Bayesian and empirical Bayesian are estimated. Then, with the help of Monte Carlo simulation and two real data sets, these estimation methods are compared.
Dr. Shahram Yaghoobzadeh Shahrestani, Dr. Reza Zarei,
Volume 25, Issue 1 (1-2021)
Abstract
Whenever approximate and initial information about the unknown parameter of a distribution is available, the shrinkage estimation method can be used to estimate it. In this paper, first, the E-Bayesian estimation of the parameter of an inverse Rayleigh distribution under the general entropy loss function is obtained. Then, the shrinkage estimate of the inverse Rayleigh distribution parameter is investigated using the guess value. Also, using Monte Carlo simulations and a real data set, the proposed shrinkage estimation is compared with the UMVU and E-Bayesian estimators based on the relative efficiency criterion.
Shahrastani Shahram Yaghoobzadeh,
Volume 27, Issue 2 (3-2023)
Abstract
In this article, the optimal model is determined for the family of models ${E_r/M/1, rin N}$ with interarrival times with Erlang distribution and service times with exponential distribution. The method of choosing the optimal model is that first, a cost function is introduced, and then a new index is introduced according to the cost function and the stationary probability of the system called $SER$. A model with a larger $SER$ index is optimal. Numerical analysis is also used to describe the method of determining optimal model.
Shahrastani Shahram Yaghoobzadeh Shahrastani, Amrollah Jafari,
Volume 28, Issue 1 (9-2023)
Abstract
In this article, queunig model $M/M/1$ is Considered, in which the innterarrival of customers have an exponenial disributon with the parameter $lambda$ and the service times have an exponenial disributon with the parameter $mu$ and are independent of the interarrival times. it is also assumed that the system is active until $T$. Then, under this stopping time Bayesian, $E$-Bayesian and hierarchical Bayesian estimations of the traffic intensity parameter of this queuing model are obtained under the general entropy loss function and considering the gamma and erlang prior distributions for parameters $lambda$ and $mu$, respicctively. Then, using numerical analysis and based on a new index, Bayesian, $E$-Bayesian and hierarchical Bayesian estimations are compared.
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