ADVANCES IN MATHEMATICAL MODELING
Year:2018 | Volume:2 | Issue:2 |Papers Count:6

In this paper, a new approach is presented for testing fuzzy hypotheses based on p-value method. In this method, we first formulate the hypotheses of interest by fuzzy sets, and then, the p-value is defined to integrate under the d-cuts of null fuzzy hypothesis. To compare the proposed p-value with the test significance level, we decide to accept or reject the null fuzzy hypothesis. Finally, the proposed method is employed by some numerical examples.

In some longitudinal studies, especially in social, economic, medical and other fields, there may be two interested responses with two different scales at a time where they may be correlated with each other. Also, considering the nature of longitudinal studies, each of the responses associated with a subject over time can also be correlated. So two correlation structure should be considered simultaneously in the data analysis. In a longitudinal study, some subjects may not be available for any reason (such as displacement, death and others), In a longitudinal study, some subjects may withdraw for any reason (such as displacement, death, etc.) and their information is not available. In this case, joint modeling of longitudinal data and drop-out event is more desirable than separate modeling of either one. In this paper, the mathematics modeling of this type of data under drop-out mechanism is presented using Bayesian approach. A Simulations study and a real data analysis is used to evaluate the performance of the proposed model. This model includes the presented models for complete data as a special case when there is no drop-out in the data set. Also, some tests for choosing the best fitted model to data are performed in the real data analysis.

In this paper, inference for stress-strength parameter of two Weibullpopulations with same shape parameters under general join progressive Type-II censoring scheme is given. First, for the parameter, the maximum likelihood estimator and bootstrap and normal approximation confidence interval are presented. Using a simulation study, the maximum likelihood estimator and bootstrap and normal approximation confidence interval are evaluated. Finally, the proposed procedures, are performed on a data set.

‎Quantile regression model and its generalizations‎, ‎including M-quantile regression model‎, ‎are analyzed usually via a nonparametric approach and their parameters are estimated using some iterative optimization algorithms‎. ‎For these reason‎, ‎in these models confidence intervals and hypotheses testing have done perforce using rank-based or bootstrapping approaches‎. ‎In this paper‎, ‎we consider parametric analysis of M-quantile model‎. ‎It is shown that‎, ‎the frequentist based approach of maximum likelihood estimation leads to results that are similar to the nonparametric approach‎. ‎Hence‎, ‎in order to achieve a more afficient model‎, ‎we have been used the Bayes theory and a‎ ‎hierarchical Bayes model has been developed‎. ‎The efficiency of the proposed model has been assessed via a simulation study and real word example‎. The ‎results ‎show ‎that ‎the ‎Bayesian ‎approach of ‎m-quantile ‎regression ‎analysis ‎is ‎more ‎efficient ‎than ‎the correspond ‎frequantist ‎appro‎ach, for all sample sizes. In addition, the proposed model truly takes into account the effect of the outlier observation, which causes skewness in response variable distribution, in modeling.

In this paper, we present a numerical method for solving nonlinear Troesch’s and Bratu’s problems. Quasilinearization process together with Haar wavelet approximation are employed to convert a nonlinear problem intoa set of linear algebraic equations. Several examples are given. We compare obtained computational results with available numerical and exact solutions found in the literature. Also numerical results are given in tables and figures and it is shown that the Haar wavelet quasilinearization (HWQ) approach is very attractive, convenient and effective.

Strong competition in today's markets has forced organizations to act as supply chain members. The supply chain member helps companies focus on specific domains and can quickly respond to changing customer needs and improve their flexibility and agility. The design of supply chain network is to provide structural design for new chains or reengineer existing networks in order to increase overall value. At this point, different decisions are made about the number of network levels, location, capacity and material flows across the network. Therefore, this paper presents a fuzzy multifunctional integer programming model that seeks to minimize costs, minimize environmental impacts, and minimize the risk of supplying raw materials. This model includes all levels of closed loop supply chain and is comprehensive with previous supply chain network design models. In order to implement the developed model, we use the data of Hamedan Glass Company. In the following, the proposed modeling mathematical model has been solved with a precise methodology, which shows the location and capacity of the facility, the amount of production in the production centers, the determination of technology.