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Showing 13 results for Subject:
Eisa Mahmoudi, Reyhaneh Lalehzari,
Volume 5, Issue 1 (9-2011)
Abstract
In this paper a new version of skew uniform distribution is introduced which is completely different from the previous works. Some important properties of the new distribution contain the expression for the density and distribution, kth moments, moment generating and characteristic functions, variance, skewness and kurtusis, mean deviation from the mean, median and mode and parameter estimation are investigated. Also a simulation study on this distribution is carried out to show the consistency of the maximum likelihood and moments estimators. In the end, the new skew uniform distribution is compared with uniform distribution.
Hamazeh Torabi, Narges Montazeri, Fatemeh Ghasemian,
Volume 7, Issue 2 (3-2014)
Abstract
In this paper, some various families constructed from the logit of the generalized Beta, Beta, Kumar, generalized Gamma, Gamma, Weibull, log gamma and Logistic distributions are reviewed. Then a general family of distributions generated from the logit of the normal distribution is proposed. A special case of this family, Normal-Uniform distribution, is defined and studied. Various properties of the distribution are also explored. The maximum likelihood and minimum spacings estimators of the parameters of this distribution are obtained. Finally, the new distribution is effectively used to analysis a real survival data set.
Saba Aghadoust, Kamel Abdollahnezhad, Farhad Yaghmaei, Ali Akbar Jafari,
Volume 9, Issue 1 (9-2015)
Abstract
The log-normal distribution is used to describe the positive data that has skewed distribution with small mean and large variance. This distribution has application in many sciences for example medicine, economics, biology and alimentary science, etc. Comparison of means of several log-normal populations always has been in focus of researchers, but their test statistics are not easy to derive or extremely complicated for this comparisons. In this paper, the size and power of different testing methods including F-test, likelihood ratio test, generalized p-value approach and computational approach test are compared in a simulation study.
Eisa Mahmoudi, Somayeh Abolhosseini,
Volume 10, Issue 1 (8-2016)
Abstract
In this paper we propose a new two-parameters distribution, which is an extension of the Lindley distribution with increasing and bathtub-shaped failure rate, called as the Lindley-logarithmic (LL) distribution. The new distribution is obtained by compounding Lindley (L) and Logarithmic distributions. We obtain several properties of the new distribution such as its probability density function, its failure rate functions, quantiles and moments. The maximum likelihood estimation procedure via a EM-algorithm is presented in this paper. At the end, in order to show the flexibility and potentiality of this new class, some series of real data is used to fit.
Eisa Mahmoudi, Reyhaneh Lalehzari, Ghahraman Roughani,
Volume 11, Issue 1 (9-2017)
Abstract
We consider the purely sequential procedure for estimating the scale parameter of an exponential distribution, when the risk function is bounded by the known preassigned number. In this paper, we provide explicit formulas for the expectation of the total sample size. Also, we propose how to adjust the stopping variable so that the risk is uniformly bounded by a known preassigned number. In the end, the performances of the proposed methodology are investigated with the help of simulations.
Rasool Roozegar, Ali Akbar Jafari,
Volume 11, Issue 1 (9-2017)
Abstract
In this paper, we introduce a family of bivariate generalized Gompertz-power series distributions. This new class of bivariate distributions contains several models such as: bivariate generalized Gompertz -geometric, -Poisson, - binomial, -logarithmic, -negative binomial and bivariate generalized exponental-power series distributions as special cases. We express the method of construction and derive different properties of the proposed class of distributions. The method of maximum likelihood and EM algorithm are used for estimating the model parameters. Finally, we illustrate the usefulness of the new distributions by means of application to real data sets.
Mohamad Bayat, Hamzeh Torabi,
Volume 12, Issue 1 (9-2018)
Abstract
Nowadays, the use of various censorship methods has become widespread in industrial and clinical tests. Type I and Type II progressive censoring are two types of these censors. The use of these censors also has some disadvantages. This article tries to reduce the defects of the type I progressive censoring by making some change to progressive censorship. Considering the number and the time of the withdrawals as a random variable, this is done. First, Type I, Type II progressive censoring and two of their generalizations are introduced. Then, we introduce the new censoring based on the Type I progressive censoring and its probability density function. Also, some of its special cases will be explained and a few related theorems are brought. Finally, the simulation algorithm is brought and for comparison of introduced censorship against the traditional censorships a simulation study was done.
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.
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.
Mohadaseh Khayyat, Rasool Rozegar, Ghobad Barmalzan,
Volume 14, Issue 1 (8-2020)
Abstract
The modified proportional hazard rates model, as one of the flexible families of distributions in reliability and survival analysis, and stochastic comparisons of (n-k+1) -out-of- n systems comprising this model have been introduced by Balakrishnan et al. (2018). In this paper, we consider the modified proportional hazard rates model with a discrete baseline case and investigate ageing properties and preservation of the usual stochastic order, hazard rate order and likelihood ratio order in this family of distributions.
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.
Hamed Salemian, Eisa Mahmoudi, Sayed Mohammad Reza Alavi,
Volume 18, Issue 1 (8-2024)
Abstract
Often, in sample surveys, respondents refused to answer some questions of a sensitive nature. Randomized response methods are designed not to reveal respondent confidentiality. In this article, a new quantitative randomized response method is introduced, and by conducting a series of simulation studies, we show that the proposed method is preferable to the cumulative and multiplicative methods. By using unbiased predictors, we estimate the covariance between two sensitive variables. In an experimental study using the proposed method, the average number of cheating and the average daily cigarette consumption of the Shahid Chamran University of Ahvaz students are estimated along with their variance, and an estimate for the covariance between them is provided.
Ali Dastbaravarde,
Volume 18, Issue 2 (2-2025)
Abstract
In statistical hypothesis testing, model misspecification error occurs when the real model of the data is none of the models under null and alternative hypotheses. This research has studied the probability of model misspecification errors in one-sided tests. These error rates are compared between the Neyman-Pearson and evidential statistical inference approaches. The results show that the evidential approach works better than the Neyman-Pearson approach.
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