List of Articles EM Algorithm

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  1 - Classical and Bayesian inference based on progressive type-II hybrid censored data from the Poisson-Exponential distribution
  masoumeh mohammadi monfared Mohammad Hassan Behzadi reza arabi belaghi
  In this paper, the problem of estimating unknown parameters is investigated when lifetime data following Poisson-exponential distribution under classical and Bayesian frameworks based on progressively type-II hybrid censored data. We compute point and associated interva More

  In this paper, the problem of estimating unknown parameters is investigated when lifetime data following Poisson-exponential distribution under classical and Bayesian frameworks based on progressively type-II hybrid censored data. We compute point and associated interval estimates under classical and Bayesian approaches. For point estimates in the problem of estimation, we compute maximum likelihood estimators of model using Expectation-Maximization (EM) and Stochastic Expectation-Maximization (SEM) algorithms under classical approach, these algorithms are easily implemented. We compute Bayes estimates with the help of Lindley and importance sampling technique under informative and non-informative priors using different loss functions namely squared error, LINEX as well as general entropy in Bayesian framework. The associated interval estimates are obtained using the Fisher information matrix and Chen and Shao method respectively under classical and Bayesian approaches. We analysis real data set, and conduct Monte Carlo simulation study for the comparison of various proposed methods. Finally, we present a conclusion. Manuscript profile
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  2 - A semiparametric first-order nonlinear autoregressive model with dependent and skew normal errors
  Leila Sakhabakhsh rahman Farnoosh Afshin Fallah Mohammad Hassan Behzadi
  10.30495/jnrm.2022.59422.2059
  The common ways for analyzing the nonlinear autoregressive models are based on normality assumption of errors, whereas in many practical situations, the residuals show a nonnormal structure. The use of these methods leads to misleading and unreliable forecasts. Also, in More

  The common ways for analyzing the nonlinear autoregressive models are based on normality assumption of errors, whereas in many practical situations, the residuals show a nonnormal structure. The use of these methods leads to misleading and unreliable forecasts. Also, in these conditions, parametric and nonparametric methods do not have the necessary efficiency in estimating the nonlinear regression function. In this paper, a first-order nonlinear autoregressive model with dependent skew normal errors is introduced and a semiparametric method is proposed to estimate the nonlinear part of model. The parameters are estimated by the maximum likelihood (ML) method using Expectation-Maximization (EM) algorithm. The performance of the proposed model is investigated by a simulation study and analysis of a real data set of daily data on the exchange rate of the euro to the dollar. Manuscript profile
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  3 - Analyzing the Polymorphism Of DISC1 Gene which Plays a Role in Cerebral Cortex Evolution and Neuro development Of Schizophrenic Patients: the First Study on an Iranian Population
  Ali Reza Pourtalebi FiroozAbadi Abasalt Hosseinzadeh Colagar Jalil Fallah Mehrabadi Gholam Reza Bidkhori
  Inroduction and Objective: This study was done with the purpose of recognizing rs2738864 polymorphism in DISC1 gene in schizophrenic patients and comparing it with healthy individuals.Material and Methods:This survey which was performed on 300 schizophrenic patients and More

  Inroduction and Objective: This study was done with the purpose of recognizing rs2738864 polymorphism in DISC1 gene in schizophrenic patients and comparing it with healthy individuals.Material and Methods:This survey which was performed on 300 schizophrenic patients and 300 healthy individuals was the first one of its type in Iran in which the patients were chosen through DSM-IV method and questioners were used to collect information. Nested allele specific PCR technic was applied in order to analyze this polymorphism and more than 50 samples were sequenced then the results were aligned with the gene sequences existing in the gene bank so as to confirm the correctness of the study. The final result was afterward statistically analyzed through SNP Analyzer and SPSS ver.22 software.Results: Finally analyzing the rs2738864 SNP in the Iranian population demonstrated that there is a tight relationship between this polymorphism and the disease; the following results were also obtained: Allele Frequency=0.629 C /0.371 T, p = 0.0000, Chi-square=46.385.Conclusion:Finally analyzing the rs2738864 SNP in the Iranian population demonstrated that there is a tight relationship between this polymorphism and the disease; the following results were also obtained: Allele Frequency=0.629 C /0.371 T, p = 0.0000, Chi-square=46.385. Manuscript profile
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  4 - The improved Semi-parametric Markov switching models for predicting Stocks Prices
  Hossein Naderi Mehrdad Ghanbari Babak Jamshidi Navid Arash Nademi
  10.22034/amfa.2021.1923297.1565
  The modelling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov More

  The modelling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov Switching models for forecasting the time series observations based on stock prices. The Semi-parametric Markov Switching models for these models are a class of popular methods that have been used extensively by researchers to increase the accuracy of fitting processes. The main part of these models is based on kernel and core functions. Despite of existence of many kernel and core functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel and exponential core function in these models. But there is a question if other types of kernel and core functions can be used in these models. This paper tries to introduce the other kernel and core functions can be offered for good fitting of the financial data. We first test three popular kernel and four core functions to find the best one and then offer the new strategy of buying and selling stocks by the best selection on these functions for real data. Manuscript profile
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  5 - A Comparative Study on Performance of "ant colony system" and "Linear Programming" methods in the Modeling of the Flow Shop Scheduling
  Said Esfandyari Ali Morovati Sharif Abadi Seyed Habibolah Mirghafouri Hamid Reza Kadkhodazadeh
  Although linear programming is used widely in the world, its inefficiency in dealing with difficult problems is concerned. With the advancement in science and dealing with various problems, it tends to have problems in mass production in a short time. Heuristic and meta More

  Although linear programming is used widely in the world, its inefficiency in dealing with difficult problems is concerned. With the advancement in science and dealing with various problems, it tends to have problems in mass production in a short time. Heuristic and meta-heuristic techniques are the latest achievements of nonlinear programming for solving the similar problem. One area that requires programming applications in mass production is NP-scheduling problems. This paper aims at modeling and comparing the two methods of Linear Programming and Ant Colony System Algorithm in flexible flow shop scheduling problem according to the number of jobs and machines. This study is based on comparing the index of time processing, the number of constraints, optimality, and the memory size of the random numbers. Using Quasi-experimental research method, software testing tools are C-sharp and Lingo for the ant colony algorithm and linear programming respectively. The results show that linear programming model has higher performance when machines and jobs are in low numbers; however, with the rise of the machines and jobs, Ant Colony System algorithm has proven high efficiency.     Manuscript profile
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  6 - Unsupervised Texture Image Segmentation Using MRFEM Framework
  Marzieh Azarian Reza Javidan Mashallah Abbasi Dezfuli
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  7 - The Improved Semi-Parametric Markov Switching Models for Predicting Stocks Prices
  hossien naderi Mehrdad Ghanbari Babak Jamshidi Navid Arash Nademi
  The modeling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov More

  The modeling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov Switching models for fore-casting the time series observations based on stock prices. The Semi-parametric Markov Switching models for these models are a class of popular methods that have been used extensively by researchers to increase the accu-racy of fitting processes. The main part of these models is based on kernel and core functions. Despite of existence of many kernel and core functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel and exponential core function in these mod-els. But there is a question if other types of kernel and core functions can be used in these models. This paper tries to introduce the other kernel and core functions can be offered for good fitting of the financial data. We first test three popular kernel and four core functions to find the best one and then offer the new strategy of buying and selling stocks by the best selection on these functions for real data. Manuscript profile