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Showing 5 results for Nasiri
Mohammad Nasirifar, Mohammadreza Akhoond, Mohammadreza Zadkarami,
Volume 13, Issue 2 (2-2020)
Abstract
The parameters of reliability for the most family marginal distribution is estimated with the assumption of independence between two component stress and strength, but, unfortunately when these two component are correlated, have been less discussed. Recently, a method based on a copula function for estimating the reliability parameter is proposed under the assumption of correlation between stress and strength components. In this paper, this method is used to estimate the reliability parameter when the distribution of componets is Generalized Exponential (GE). For this purpose FGM, generalized FGM and frank copula function have been used. Then simulation is also used to demonstrate the suitability of the estimates. In the end, reliability parameter for data relative contribution of major groups in terms of age breakdown of the population of urban and rural areas in Iran in the year 1390 will be estimated.
Ali Sakhaei, Parviz Nasiri,
Volume 13, Issue 2 (2-2020)
Abstract
The non-homogeneous bivariate compound Poisson process with short term periodic intensity function is used for modeling the events with seasonal patterns or periodic trends. In this paper, this process is carefully introduced. In order to characterize the dependence structure between jumps, the Levy copula function is provided. For estimating the parameters of the model, the inference for margins method is used. As an application, this model is fitted to an automobile insurance dataset with inference for margins method and its accuracy is compared with the full maximum likelihood method. By using the goodness of fit test, it is confirmed that this model is appropriate for describing the data.
Ehsan Golzade Gervi, Parviz Nasiri, Mahdi Salehi,
Volume 15, Issue 1 (9-2021)
Abstract
The empirical Bayes estimation of the exponential distribution parameter under squared error and LINEX loss functions is investigated when the record collects the data ranked set sampling scheme method. Then, point and interval predictions for future record values are studied. The results of this sampling scheme are compared with the products of the inverse sampling scheme. To compare the accuracy of estimators, Bayes risk and posterior risk criteria are used. These point predictors are compared in the sense of their mean squared prediction errors. To evaluate the prediction intervals for both the sampling schemes, the average interval length and coverage probability are computed and compared. In the present study, the hyperparameters are estimated in two methods. By studying the simulation and presenting real data, the estimation methods are compared, and the performance of the introduced schemes is evaluated.
Parviz Nasiri, Raouf Obeidi,
Volume 16, Issue 1 (9-2022)
Abstract
This paper presents the inverse Weibull-Poisson distribution to fit censored lifetime data. The parameters of scale, shape and failure rate are considered in terms of estimation and hypothesis testing, so the parameters are estimated under the type-II of censorship using the maximum likelihood and Bayesian methods. In Bayesian analysis, the parameters are estimated under different loss functions. The simulation section presents the symmetric confidence interval and HPD, and the estimators are compared using statistical criteria. Finally, the model's goodness of fit is evaluated using an actual data set.
Mr Reza Zabihi Moghadam, Dr Masoud Yarmohammadi, Dr Hossein Hassani, Dr Parviz Nasiri,
Volume 16, Issue 2 (3-2023)
Abstract
The Singular Spectrum Analysis (SSA) method is a powerful non-parametric method in the field of time series analysis and has been considered due to its features such as no need to stationarity assumptions or a limit on the number of collected observations. The main purpose of the SSA method is to decompose time series into interpretable components such as trend, oscillating component, and unstructured noise. In recent years, continuous efforts have been made by researchers in various fields of research to improve this method, especially in the field of time series prediction. In this paper, a new method for improving the prediction of singular spectrum analysis using Kalman filter algorithm in structural models is introduced. Then, the performance of this method and some generalized methods of SSA are compared with the basic SSA using the root mean square error criterion. For this comparison, simulated data from structural models and real data of gas consumption in the UK have been used. The results of this study show that the newly introduced method is more accurate than other methods.
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