Shahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80884120140823variable selection of generalized semi-parametric mixture modelsvariable selection of generalized semi-parametric mixture models12611045FAFarzadEskandariDepartment of Statistics, Allameh Tabataba’i UniversityEhsanOrmozDepartment of Statistics, Mashhad branch, Islamic Azad University0000-0003-3557-5755RahmanFarnooshSchool of Mathematics, Iran University of Science and TechnologyJournal Article20140211Purpose of this paper is identifying best covariates of a semi-parametric model in the presence of penalized coefficients. It should be noted that in each model, coefficients of the existing variables is considered as a combination of parameters where some of them affect the response variable linearly and some of them functionally. So, semi-parametric method was considered as an optimum solution.<br />In this paper we concerned with variable selection in finite mixture of generalized semi-parametric models. This task consists of model selection for nonparametric component and variable selection for parametric part. Thus we encounter with separate model selection for each nonparametric component of each sub model. To overcome to this computational burden, we introduce a class of variable selection procedures for finite mixture of generalized semi-parametric models. It is shown that the new method is consistent for variable selection. Simulations show that the performance of proposed method is good and improve pervious works in this area and also requires much less computing power than existing methods.Purpose of this paper is identifying best covariates of a semi-parametric model in the presence of penalized coefficients. It should be noted that in each model, coefficients of the existing variables is considered as a combination of parameters where some of them affect the response variable linearly and some of them functionally. So, semi-parametric method was considered as an optimum solution.<br />In this paper we concerned with variable selection in finite mixture of generalized semi-parametric models. This task consists of model selection for nonparametric component and variable selection for parametric part. Thus we encounter with separate model selection for each nonparametric component of each sub model. To overcome to this computational burden, we introduce a class of variable selection procedures for finite mixture of generalized semi-parametric models. It is shown that the new method is consistent for variable selection. Simulations show that the performance of proposed method is good and improve pervious works in this area and also requires much less computing power than existing methods.https://jamm.scu.ac.ir/article_11045_b0b3670b8ce6de06426cc07d76253098.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80884120140823Enhancing the solution method of linear Bi – level programming problem based on enumeration method and dual methodEnhancing the solution method of linear Bi – level programming problem based on enumeration method and dual method275311214FAIsaNakhai KamalabadiDepartment of Industrial Engineering, University of KurdistanEghbalHosseiniPayamenur University of Tehran, Department of MathematicsMohammadFathiDepartment of Power Engineering, University of KurdistanJournal Article20140724In the recent years, the bi-level programming problem (BLPP) is known as an appropriate approach for solving the real problems in applicable areas such as traffic, transportation, economics and supply chain management. There are several known algorithms to solve BLPP as an NP-hard problem. Almost all proposed algorithms in references have been used the Karush-Kuhn–Tucker to convert the BLPP into the single level problem which the obtained problem is complicated. In this paper, we attempt to develop two effective approaches, one based on enumeration method and the other based on duality characteristic for solving the linear BLPP. In these approaches, the BLPP is solved without using the Karush-Kuhn–Tucker conditions. The presented approaches achieve an efficient and feasible solution in an appropriate time which has been evaluated by comparing to references and test problems.In the recent years, the bi-level programming problem (BLPP) is known as an appropriate approach for solving the real problems in applicable areas such as traffic, transportation, economics and supply chain management. There are several known algorithms to solve BLPP as an NP-hard problem. Almost all proposed algorithms in references have been used the Karush-Kuhn–Tucker to convert the BLPP into the single level problem which the obtained problem is complicated. In this paper, we attempt to develop two effective approaches, one based on enumeration method and the other based on duality characteristic for solving the linear BLPP. In these approaches, the BLPP is solved without using the Karush-Kuhn–Tucker conditions. The presented approaches achieve an efficient and feasible solution in an appropriate time which has been evaluated by comparing to references and test problems.https://jamm.scu.ac.ir/article_11214_7e521bae700ef0bc5815247aceba9186.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80884120140823Inference of Spatial Generalized Linear Mixed Models using Integrated Laplace Nested ApproximationInference of Spatial Generalized Linear Mixed Models using Integrated Laplace Nested Approximation556910772FAFatemehHosseiniDepartment of Statistics, University of Semnan0000-0003-1826-4090OmidKarimiDepartment of Statistics, University of SemnanMonavarMohammad KarimiDepartment of Statistics, University of SemnanJournal Article20140615Spatial generalized linear mixed models are used for modeling geostatistical discrete spatial responses and spatial correlation of the data is considered via latent variables. The most important interest in these models is estimation of the model parameters and the prediction of the latent variables. In this paper, first a prediction method is presented and then, Bayesian approach and MCMC algorithms are intrpretation. Since these models are complex and in the Bayes inference of these models, are used Monte Carlo sampling, computation time is long. The Approximatin Baysian methods are considered for solving this problem. Finally, the proposed methods are applied to a case study on rainfall data observed in the weather stations of Semnan in 1391.Spatial generalized linear mixed models are used for modeling geostatistical discrete spatial responses and spatial correlation of the data is considered via latent variables. The most important interest in these models is estimation of the model parameters and the prediction of the latent variables. In this paper, first a prediction method is presented and then, Bayesian approach and MCMC algorithms are intrpretation. Since these models are complex and in the Bayes inference of these models, are used Monte Carlo sampling, computation time is long. The Approximatin Baysian methods are considered for solving this problem. Finally, the proposed methods are applied to a case study on rainfall data observed in the weather stations of Semnan in 1391.https://jamm.scu.ac.ir/article_10772_dd0c1fedfed8ba59ab30b0e82fa1778a.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80884120140823Admissibility of lifetime performance index with respect to weighted squared-error loss function in Pareto distribution under progressive type II right censored sampleAdmissibility of lifetime performance index with respect to weighted squared-error loss function in Pareto distribution under progressive type II right censored sample718410901FAMaryamSheikhalishahiDepartment of Statistics, University of YazdHojatollahZakerzadehDepartment of Statistics, University of YazdJournal Article20140420In this paper, under the assumption of Pareto distribution, construct a maximum likelihood estimator, UMVUE and also, assuming the Exponential prior distribution and weighted squared-error loss function, this study construct Bayes and Empirical Bayes estimator of C_L based on the progressive type II right censored sample. An admissible estimator of C_L is given for Pareto distribution with respect to the weighted squared-error loss function. The MLE and Bayes estimator of C_L is then utilize to develop a confidence and credible interval. Moreover, we also propose a likelihood Ratio Tests and a Bayesian Test to assess the lifetime performance index. Finally, we give one example to illustrate the use of testing procedure under given significance level.In this paper, under the assumption of Pareto distribution, construct a maximum likelihood estimator, UMVUE and also, assuming the Exponential prior distribution and weighted squared-error loss function, this study construct Bayes and Empirical Bayes estimator of C_L based on the progressive type II right censored sample. An admissible estimator of C_L is given for Pareto distribution with respect to the weighted squared-error loss function. The MLE and Bayes estimator of C_L is then utilize to develop a confidence and credible interval. Moreover, we also propose a likelihood Ratio Tests and a Bayesian Test to assess the lifetime performance index. Finally, we give one example to illustrate the use of testing procedure under given significance level.https://jamm.scu.ac.ir/article_10901_9de6dc115d87bd25a7f68c4767690ead.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80884120140823Comparison of the bootstrap and bootstrap neural network methods in non linear time seriesComparison of the bootstrap and bootstrap neural network methods in non linear time series8510611046FAMasoudIsaparehDepartment of Statistics, University of IsfahanNasrollahIranpanahDepartment of Statistics, University of IsfahanMarjanKaediDepartment of Information Technology Engineering , University of IsfahanJournal Article20140723Neural networks are among those mathematical models which are used to model non-linear time series with high accuracy. The advantage with these linear times series as opposed to topical ones is that they don’t require restrictive assumptions. The accuracy of neural network based estimators as nonparametric models is of high importance. In that light, we can use bootstrapping to calculate the accuracy of estimators in the time series’ complex nonlinear structures. Though introduced in recent years these methods yield more accurate results in the bias calculation of estimators compared to the other ones. This paper introduces neural network bootstrap, bootstrap autoregressive, moving block bootstrap method and residual bootstrap methods in time series. Then these four algorithms are compared with each other in a simulation study. Finally an example related to Iran’s kerosene price monthly data is worked out.Neural networks are among those mathematical models which are used to model non-linear time series with high accuracy. The advantage with these linear times series as opposed to topical ones is that they don’t require restrictive assumptions. The accuracy of neural network based estimators as nonparametric models is of high importance. In that light, we can use bootstrapping to calculate the accuracy of estimators in the time series’ complex nonlinear structures. Though introduced in recent years these methods yield more accurate results in the bias calculation of estimators compared to the other ones. This paper introduces neural network bootstrap, bootstrap autoregressive, moving block bootstrap method and residual bootstrap methods in time series. Then these four algorithms are compared with each other in a simulation study. Finally an example related to Iran’s kerosene price monthly data is worked out.https://jamm.scu.ac.ir/article_11046_8cee2eb66efa288bef544c98a6ba8ae1.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80884120140823Comparison of Confidence Interval Based on Bootstrap Method for the Mean Response time in a Simulation StudyComparison of Confidence Interval Based on Bootstrap Method for the Mean Response time in a Simulation Study10713010773FAHosseinKazemzadeDepartment of Mathematics, Maku Branch, Islamic Azad UniversityBahmanTarvirdizadeDepartment of Mathematics, Maku Branch, Islamic Azad UniversityAlirezaAfsharisafaviDepartment of Mathematics, Maku Branch, Islamic Azad UniversityJournal Article20140113The mean response time plays an important role in the analyzing and optimizing the queuing system which determines the number and type of giving service. In this paper, new confidence intervals of mean response time for an M/G/1 FCFS queuing system is contrasted based on the nonparametric delta method and five bootstrap methods. These methods include: nonparametric delta method confidence interval based on the influence function, standard bootstrap confidence interval, percentile bootstrap confidence interval, bootstrap-t confidence interval, bias corrected and acceleration bootstrap confidence interval and bootstrap pivotal confidence interval. In a simulation study, these six methods are compared and evaluated the accuracy and performance of the confidence intervals for three different M/G/1 FCFS queuing systems based on the coverage percentage and the average length of confidence intervals.The mean response time plays an important role in the analyzing and optimizing the queuing system which determines the number and type of giving service. In this paper, new confidence intervals of mean response time for an M/G/1 FCFS queuing system is contrasted based on the nonparametric delta method and five bootstrap methods. These methods include: nonparametric delta method confidence interval based on the influence function, standard bootstrap confidence interval, percentile bootstrap confidence interval, bootstrap-t confidence interval, bias corrected and acceleration bootstrap confidence interval and bootstrap pivotal confidence interval. In a simulation study, these six methods are compared and evaluated the accuracy and performance of the confidence intervals for three different M/G/1 FCFS queuing systems based on the coverage percentage and the average length of confidence intervals.https://jamm.scu.ac.ir/article_10773_6e1eecefca525529df9aba53368b5892.pdf