Shahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80881220110923Spatial Logistic and Probit Regression Models for Analysis of Frosting Data in Mazandaran ProvinceSpatial Logistic and Probit Regression Models for Analysis of Frosting Data in Mazandaran Province11710019FAVahidRezaeitabarDepartment of Statistics, Tarbiat Modares University, Tehran, IranMohsenMohammadzadehDepartment of Statistics, Tarbiat Modares University, Tehran, Iran0000-0002-2361-6145Journal Article20130610Logistic and Probit regression models are usually used in binary response variable analysis based on independence assumption of the observations. But, in practice, there are many situations in which, due to their different locations in the underling space of study, this assumption dose not satisfies. In spatial statistics, it is generally supposed that the binary observations are analyzed with indicator Kriging. In this paper, we considered the spatial Logistic and Probit regression models with auto correlated errors on a rectangular grid. Also, in a simulation study, the prediction accuracy of the models has been compared. Finally, the implementation of the models for a temperature data set, reported by weather stations in Mazandaran province of Iran, is shown.Logistic and Probit regression models are usually used in binary response variable analysis based on independence assumption of the observations. But, in practice, there are many situations in which, due to their different locations in the underling space of study, this assumption dose not satisfies. In spatial statistics, it is generally supposed that the binary observations are analyzed with indicator Kriging. In this paper, we considered the spatial Logistic and Probit regression models with auto correlated errors on a rectangular grid. Also, in a simulation study, the prediction accuracy of the models has been compared. Finally, the implementation of the models for a temperature data set, reported by weather stations in Mazandaran province of Iran, is shown.https://jamm.scu.ac.ir/article_10019_ea1b95bd6bd099f19dcaf86f843ea2c4.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80881220110923Powers of Irreducible Characters of Finite GroupsPowers of Irreducible Characters of Finite Groups192710020FAMohammad RezaDarafshehSchool of Mathematics, Statistics and Computer Science, College of
Science, University of Tehran, Tehran, IranEmadZahediSchool of Mathematics, Statistics and Computer Science, College of
Science, University of Tehran, Tehran, IranJournal Article20130610Let X be an irreducible character of a non-abelian group G. For non-negative integers n, m such that m+n>0 , we study the case when all the irreducible constituents of X<sup>n</sup>X<sup>m</sup> are linear. Mann proved that if G is a finite non-abelian group with an irreducible character X such that all the irreducible constituents of <br />X<sup>2</sup> are linear, then G<Z(G) and as a consequence G is nilpotent. In this paper we generalize the result of Mann and prove that if m, n are non-negative integers with m+n>0 , and if X is an irreducible character of G, then all the irreducible constituents of X<sup>n</sup>X<sup>m</sup> are linear if and only if G<Z(G).Let X be an irreducible character of a non-abelian group G. For non-negative integers n, m such that m+n>0 , we study the case when all the irreducible constituents of X<sup>n</sup>X<sup>m</sup> are linear. Mann proved that if G is a finite non-abelian group with an irreducible character X such that all the irreducible constituents of <br />X<sup>2</sup> are linear, then G<Z(G) and as a consequence G is nilpotent. In this paper we generalize the result of Mann and prove that if m, n are non-negative integers with m+n>0 , and if X is an irreducible character of G, then all the irreducible constituents of X<sup>n</sup>X<sup>m</sup> are linear if and only if G<Z(G).https://jamm.scu.ac.ir/article_10020_5d7b0e09c06e60bdfbe7f2b65ef2965e.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80881220110923Application a Modified Imperialist Competitive Algorithm for Solving the Traveling Salesman ProblemApplication a Modified Imperialist Competitive Algorithm for Solving the Traveling Salesman Problem294910021FAMajidYousefikhoshbakhtYoung Researchers Club, Hamedan Branch, Islamic Azad University,
Hamedan, IranFarzadDidehvarCollege of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, IranFarhadRahmatiCollege of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, IranJournal Article20130610This paper proposes a modified Imperialist Competitive Algorithm (MICA) for solving the Traveling Salesman Problem (TSP) that is different with common Imperialist Competitive Algorithm (ICA) in assimilation policy between Imperialist and colonies countries and revolution of colonies. Furthermore, the 3-opt local search is used for increasing performance of the algorithm. The new ICA algorithm is tested on nineteen instances of TSBLIB and its performance is compared with ICA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Evolutionary Algorithm (EA) and Bee Colony Optimization (BCO). Extensive computational tests confirm the effectiveness of the proposed approach.This paper proposes a modified Imperialist Competitive Algorithm (MICA) for solving the Traveling Salesman Problem (TSP) that is different with common Imperialist Competitive Algorithm (ICA) in assimilation policy between Imperialist and colonies countries and revolution of colonies. Furthermore, the 3-opt local search is used for increasing performance of the algorithm. The new ICA algorithm is tested on nineteen instances of TSBLIB and its performance is compared with ICA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Evolutionary Algorithm (EA) and Bee Colony Optimization (BCO). Extensive computational tests confirm the effectiveness of the proposed approach.https://jamm.scu.ac.ir/article_10021_1d60ca49f2f60218ae4048939f0ea68e.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80881220110923Simulation of the breathing gases in the airwaysSimulation of the breathing gases in the airways516610022FAAzimAmin AminataeiDepartment of Mathematics, K.N. Toosi University of Technology, Tehran, IranJournal Article20130610In this study, we have simulated the breathing of gases in the human airways, and the simulated equation is solved in two different ways. It is observed that the numerical solution is more complicated in comparison with the analytic one.In this study, we have simulated the breathing of gases in the human airways, and the simulated equation is solved in two different ways. It is observed that the numerical solution is more complicated in comparison with the analytic one.https://jamm.scu.ac.ir/article_10022_5cdaf4f52de9fb3e876f13fb9b7ea0fb.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80881220110923Goodness-of-fit tests for the weighted exponential distributionGoodness-of-fit tests for the weighted exponential distribution678310023FAMohammad MehdiMaghamiDepartment of Statistics, University of Isfahan, Isfahan, IranNasrollahIranpanahDepartment of Statistics, University of Isfahan, Isfahan, IranJournal Article20130610In the new class of weighted exponential distributions was presented by Gupta and Kundu [1], the skewness parameter has been added to the exponential distribution. Therefore the weighted exponential distribution has the skewness and scale parameters. In this paper, we first study Anderson and Kolmogorov-Smirnov goodness of fit tests for this class with unknown parameters. Then, we apply bootstrap method for estimation of Anderson’s quantile and another method for Kolmogorov-Smirnov. We use the maximum likelihood method for estimation of parameters. Finally, we compare Kolmogorov-Smirnov and Anderson tests in a Monte Carlo simulation study. The results show that the Kolmogorov–Smirnov test has greater power than Anderson test.In the new class of weighted exponential distributions was presented by Gupta and Kundu [1], the skewness parameter has been added to the exponential distribution. Therefore the weighted exponential distribution has the skewness and scale parameters. In this paper, we first study Anderson and Kolmogorov-Smirnov goodness of fit tests for this class with unknown parameters. Then, we apply bootstrap method for estimation of Anderson’s quantile and another method for Kolmogorov-Smirnov. We use the maximum likelihood method for estimation of parameters. Finally, we compare Kolmogorov-Smirnov and Anderson tests in a Monte Carlo simulation study. The results show that the Kolmogorov–Smirnov test has greater power than Anderson test.https://jamm.scu.ac.ir/article_10023_a4ab317ec8eeb7d86177f30ece7cfa0f.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80881220110923Bayesian Analysis of Random-Intercept Models with the Skew-Laplace DistributionBayesian Analysis of Random-Intercept Models with the Skew-Laplace Distribution8510110024FARaziehMohammadiDepartment of Statistics, University of Isfahan, Isfahan, IranIrajKazemiDepartment of Statistics, University of Isfahan, Isfahan, IranJournal Article20130610In fitting random-intercept models, it is commonly assumed that the random effects and the error terms follow the normal distribution. In many empirical applications, the true distribution of data obeys non-normality and thus the main concern of most recent studies is the use of alternative distributions. In this paper, we propose a new class of random-intercept models using the Skew-Laplace distribution. The new regression model is flexible in the analysis of correlated data and simple in the implementation of Markov Chain Monte Carlo methods, such as the Gibbs sampling approach. Using the stochastic representation of the Skew-Laplace distribution we derive the full conditional posteriors distributions in order to present the Bayesian inference of model parameters. A real data analysis is illustrated from the economic contexts to show the usefulness of the proposed model.In fitting random-intercept models, it is commonly assumed that the random effects and the error terms follow the normal distribution. In many empirical applications, the true distribution of data obeys non-normality and thus the main concern of most recent studies is the use of alternative distributions. In this paper, we propose a new class of random-intercept models using the Skew-Laplace distribution. The new regression model is flexible in the analysis of correlated data and simple in the implementation of Markov Chain Monte Carlo methods, such as the Gibbs sampling approach. Using the stochastic representation of the Skew-Laplace distribution we derive the full conditional posteriors distributions in order to present the Bayesian inference of model parameters. A real data analysis is illustrated from the economic contexts to show the usefulness of the proposed model.https://jamm.scu.ac.ir/article_10024_44a5894e3c65ae4c45a6b7c176aa9140.pdf