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Showing 5 results for Monte Carlo Simulation.
Hossein Nadeb, Hamzeh Torabi,
Volume 13, Issue 1 (9-2019)
Abstract
In this paper, a general method for goodness of fit test for the location-scale family of distributions under Type-II progressive censoring is presented and its properties are investigated. Then, using Monte Carlo simulation studies, the power of this test is compared with the powers of some existing tests for testing the Gumbel distribution. Finally the proposed test is used for fitting a distribution to a real data set.
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.
Hoda Kamranfar, Javad Etminan, Majid Chahkandi,
Volume 14, Issue 2 (2-2021)
Abstract
A repairable system with two types of failures is studied. Type I failure (minor failure) is removed by a minimal repair, whereas type II failure (catastrophic failure) is modified by an unplanned replacement. The first failure of the system follows a Weibull probability distribution and two maintenance policies are considered. In the first policy, the system is replaced at time T or the first type II failure, and in the second policy, the system is replaced at the nth type I failure, the first type II failure or at time T, whichever takes place first. This paper aims to derive a general representation for the likelihood function of the proposed models. The likelihood-ratio test statistic, maximum likelihood estimators and asymptotic confidence intervals for the parameters are also found. Finally, a Monte Carlo simulation is conducted to illustrate the results.
Eisa Mahmoudi, Soudabeh Sajjadipanah, Mohammad Sadegh Zamani,
Volume 16, Issue 1 (9-2022)
Abstract
In this paper, a modified two-stage procedure in the Autoregressive model AR(1) is considered, which investigates the point and the interval estimation of the mean based on the least-squares estimator. The modified two-stage procedure is as effective as the best fixed-sample size procedure. In this regard, the significant properties of the procedure, including asymptotic risk efficiency, first-order efficiency, consistent, and asymptotic distribution of the mean, are established. Then, a Monte Carlo simulation study is deduced to investigate the modified two-stage procedure. The performance of estimators and confidence intervals are evaluated utilizing a simulation study. Finally, real-time series data is considered to illustrate the applicability of the modified two-stage procedure.
Zahra Zandi, Hossein Bevrani,
Volume 16, Issue 2 (3-2023)
Abstract
This paper suggests Liu-type shrinkage estimators in linear regression model in the presence of multicollinearity under subspace information. The performance of the proposed estimators is compared to Liu-type estimator in terms of their relative efficiency via a Monte Carlo simulation study and a real data set. The results reveal that the proposed estimators outperform better than the Liu-type estimator.
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