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:: Search published articles ::
Showing 6 results for Zare

Mehran Naghizadeh Qomi, Zohre Mahdizadeh, Hamid Zareefard,
Volume 12, Issue 1 (9-2018)
Abstract

Suppose that we have a random sample from one-parameter Rayleigh distribution‎. ‎In classical methods‎, ‎we estimate the interesting parameter based on the sample information and‎ ‎with usual estimators‎. ‎Sometimes in practice‎, ‎the researcher has some information about the unknown‎ ‎parameter in the form of a guess value‎. ‎This guess is known as nonsample information‎. ‎In this case‎, ‎linear shrinkage estimators are introduced‎ ‎by combining nonsample and sample information which have smaller risk than usual estimators in the vicinity of‎ ‎guess and true value‎. ‎In this paper‎, ‎some shrinkage testimators are introduced using different methods based on‎ ‎vicinity of guess value and true parameter and their risks are computed under the entropy loss function‎. ‎Then‎, ‎the performance of‎ ‎shrinkage testimators and the best linear estimator is calculated via the relative efficiency of them‎. ‎Therefore‎, ‎the results are applied for the type-II censored data.


Marjan Zare, Akbar Asgharzadeh, Seyed Fazel Bagheri,
Volume 14, Issue 1 (8-2020)
Abstract

In this paper, the smallest confidence region is obtained for the location and scale parameters of the two-parameter exponential distribution. For this purpose, we use constrained optimization problems. We first provide some suitable pivotal quantities to obtain a balanced confidence region. We then obtain the smallest confidence region by minimizing the area of the confidence region using the Lagrangian method. Two numerical examples are presented to illustrate the proposed methods. Finally, some applications of proposed joint confidence regions in hypothesis testing and the construction of confidence bands are discussed.

Reza Zarei, ,
Volume 14, Issue 2 (2-2021)
Abstract

In this paper, the Bayesian and empirical Bayesian approaches studied in estimate the multicomponent stress–strength reliability model when the strength and stress variables have a generalized Rayleigh distribution with different shape parameters and identical scale parameter. The Bayesian, empirical Bayesian and maximum likelihood estimation of reliability function is obtained in the two cases known and unknown of scale parameter under  the mean squared error loss function. Then, these estimators are compared empirically using Monte Carlo simulation and two real data sets.

Shaho Zarei,
Volume 15, Issue 2 (3-2022)
Abstract

The most widely used model in small area estimation is the area level or the Fay-Herriot model. In this model, it is typically assumed that both the area level random effects (model errors) and the sampling errors have a Gaussian distribution.  However, considerable variations in error components (model errors and sampling errors) can cause poor performance in small area estimation. In this paper, to overcome this problem, the symmetric α-stable distribution is used to deal with outliers in the error components. The model parameters are estimated with the empirical Bayes method. The performance of the proposed model is investigated in different simulation scenarios and compared with the existing classic and robust empirical Bayes methods. The proposed model can improve estimation results, in particular when both error components are normal or have heavy-tailed distribution.


Mozhgan Moradi, Shaho Zarei,
Volume 18, Issue 1 (8-2024)
Abstract

Model-based clustering is the most widely used statistical clustering method, in which heterogeneous data are divided into homogeneous groups using inference based on mixture models. The presence of measurement error in the data can reduce the quality of clustering and, for example, cause overfitting and produce spurious clusters. To solve this problem, model-based clustering assuming a normal distribution for measurement errors has been introduced. However, too large or too small (outlier) values ​​of measurement errors cause poor performance of existing clustering methods. To tackle this problem {and build a stable model against the presence of outlier measurement errors in the data}, in this article, a symmetric $alpha$-stable distribution is proposed as a replacement for the normal distribution for measurement errors, and the model parameters are estimated using the EM algorithm and numerical methods. Through simulation and real data analysis, the new model is compared with the MCLUST-based model, considering cases with and without measurement errors, and the performance of the proposed model  for data clustering in the presence of various outlier measurement errors is shown.
Elham Ranjbar, Mohamad Ghasem Akbari, Reza Zarei,
Volume 19, Issue 1 (9-2025)
Abstract

In the time series analysis, we may encounter situations where some elements of the model are imprecise quantities. One of the most common situations is the inaccuracy of the underlying observations, usually due to measurement or human errors. In this paper, a new fuzzy autoregressive time series model based on the support vector machine approach is proposed. For this purpose, the kernel function has been used for the stability and flexibility of the model, and the constraints included in the model have been used to control the points. In order to examine the performance and effectiveness of the proposed fuzzy autoregressive time series model, some goodness of fit criteria are used. The results were based on one example of simulated fuzzy time series data and two real examples, which showed that the proposed method performed better than other existing methods.

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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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