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Hossein Naraghi, Ali Iranmanesh,
Volume 2, Issue 1 (8-2008)
Abstract
In this paper, first we define the commutativity of two fuzzy subgroups, and then we computed the probability of commutativity of the group Zpn which its support is exactly Zp m for m<=n.
Jalal Chachi, Gholamreza Hesamian,
Volume 8, Issue 1 (9-2014)
Abstract
In this paper, we deal with modeling crisp input-fuzzy output data by constructing a MARS-fuzzy regression model with crisp parameters estimation and fuzzy error terms for the fuzzy data set. The proposed method is a two-phase procedure which applies the MARS technique at phase one and an optimization problem at phase two to estimate the center and fuzziness of the response variable. A realistic application of the proposed method is also presented in a hydrology engineering problem. Empirical results demonstrate that the proposed approach is more efficient and more realistic than some traditional least-squares fuzzy regression models.
Mrs Manije Sanei Tabass, Professor Gholamreza Mohtashami Borzadaran,
Volume 11, Issue 1 (9-2017)
Abstract
Maximum of the Renyi entropy and the Tsallis entropy are generalization of the maximum entropy for a larger class of Shannon entropy. In this paper we introduce the maximum Renyi entropy and some of the attributes of distributions which have maximum Renyi entropy investigated. The form of distributions with maximum Renyi entropy is power so we state some properties of these distributions and we have a new form of the Renyi entropy. After pointing the topics of minimum Renyi divergence, some other points in this relation have been discussed. An another form of Renyi divergence have also obtained. Therefore we discussed some of the economic applications of the maximum entropy. Meanwhile, the review of the Csiszar information measure, the general form of distributions with minimum Renyi divergence have obtained.
Ghasem Rekabdar, Rahim Chinipardaz, Behzad Mansouri,
Volume 13, Issue 1 (9-2019)
Abstract
In this study, the multi-parameter exponential family of distribution has been used to approximate the distribution of indefinite quadratic forms in normal random vectors. Moments of quadratic forms can be obtained in any orders in terms of representation of the quadratic forms as weighted sum of non-central chi-square random variables. By Stein's identity in exponential family, we estimated parameters of probability density function. The method handled in some examples and we indicated this method suitable for approximating the quadratic form distribution.
Zahra Rahimian Azad, Afshin Fallah,
Volume 15, Issue 1 (9-2021)
Abstract
This paper considers the Bayesian model averaging of inverse Gaussian regression models for regression analysis in situations that the response observations are positive and right-skewed. The computational challenges related to computing the essential quantities for executing of this methodology and their dominating ways are discussed. Providing closed form expressions for the interested posterior quantities by considering suitable prior distributions is an attractive aspect of the proposed methodology. The proposed approach has been evaluated via a simulation study and its applicability is expressed by using a real example related to the seismic studies.
Jalal Chachi, Alireza Chaji,
Volume 15, Issue 1 (9-2021)
Abstract
This article introduces a new method to estimate the least absolutes linear regression model's parameters, which considers optimization problems based on the weighted aggregation operators of ordered least absolute deviations. In the optimization problem, weighted aggregation of orderd fitted least absolute deviations provides data analysis to identify the outliers while considering different fitting functions simultaneously in the modeling problem. Accordingly, this approach is not affected by outlier observations and in any problem proportional to the number of potential outliers selects the best model estimator with the optimal break-down point among a set of other candidate estimators. The performance and the goodness-of-fit of the proposed approach are investigated, analyzed and compared in modeling analytical dataset and a real value dataset in hydrology engineering at the presence of outliers. Based on the results of the sensitivity analysis, the properties of unbiasedness and efficiency of the estimators are obtained.
Meisam Moghimbeygi,
Volume 16, Issue 2 (3-2023)
Abstract
This article introduces a semiparametric multinomial logistic regression model to classify labeled configurations. In the regression model, the explanatory variable is the kernel function obtained using the power-divergence criterion. Also, the response variable was categorical and showed the class of each configuration. This semiparametric regression model is introduced based on distances defined in the shape space, and for this reason, the correct classification of shapes using this method has been improved compared to previous methods. The performance of this model has been investigated in the comprehensive simulation study. Two real datasets were analyzed using this article's method as an application. Finally, the method presented in this article was compared with the techniques introduced in the literature, which shows the proper performance of this method in classifying configurations.
Mohammad Shafaei Noughabi, Mohammad Khorashadizade,
Volume 19, Issue 1 (9-2025)
Abstract
This article introduces a new extension of the log-logistic distribution, and its properties and parameter estimation are studied and analyzed. It is shown that adding a parameter to this distribution makes its shape more symmetric and less skewed as the parameter increases. Unlike the original distribution, the moments of the new distribution and its quantile function always exist. Furthermore, it is demonstrated that the reliability measures, such as the hazard rate function, the mean residual life function, and stochastic orderings, are more flexible in the new distribution. Additionally, the parameters of the distribution are estimated using the LLP and ML methods, and the efficiency and consistency of the estimators are evaluated through simulation studies. Finally, the practical applicability of the model is demonstrated by applying the new model to real-world data from airborne equipment and lung cancer patients.
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