IRAQI JOURNAL OF STATISTICAL SCIENCES

In many applications of time series , the white noise does not follow the normal distribution but follows one of the heavy tailed distributions. When
using normal models in estimation and forecasting, these phenomena they will produce far from reality and inefficient estimators and predictions. The generalized multivariate modified Bessel distribution belongs to the potentially heavy-tailed distribution family and has wide applications in events that change over time. On this basis, this paper concerned with the study of vector moving average model of the first order (VMA(1)), is the white noise error term of this model follows GMMB. The non linear VMA(1) model was approximated to a linear model. The parameters of approximated model was estimated by Bayesian technique when non-informative priors. We supposed that some parameters of the distribution ( ) known.
Different loss functions has been used in Bayesian analysis, We proposed two positive weight functions in weighted balanced loss functions .
Some of theoretical results were applied on empirical sample generated from VMA(1) model. It is concluded that the estimators under proposed weighted balanced loss functions are better.

The theory of reliability is one of the important theories that is attracting great interest in many researchers at present because of its importance in various fields of life especially in determining the age of a particular device and its efficiency and the addition of applications in the field of radar and atmospheric and astronomical observation. The growing interest in reliability studies is due to the rapid technical and technological development and the use of complex systems in various fields. This paper reviews the article of reliability in terms of the reliability function for complete data  of one of the failure time distributions (weibull distribution) and its parameters in different methods using simulation.

Variable selection is a very helpful procedure for improving prediction accuracy by finding the most important variables that are related to the response variable. Poisson regression model has received much attention in several science fields for modeling count data. Invasive weed optimization algorithm (IWO) is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, IWO algorithm is proposed to perform variable selection for Poisson regression model. Extensive simulation studies and real data application are conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. The results proved the efficiency of our proposed methods and it outperforms other popular methods.

Meta-heuristic algorithms have become an arising field of research in recent years. Some of these algorithms have proved to be efficient in solving combinatorial optimization problems, particularly knapsack problem. In this paper, four meta-heuristic algorithms are presented particle swarm optimization, firefly algorithm, flower pollination algorithm and monarch butterfly optimization in solving knapsack problem as example of NP-hard combinational optimization problems. Based on twenty 0-1 knapsack problem instances, the computational results demonstrated that the binary flower pollination algorithm has the ability to find the best solutions in reasonable time.
 

The data tend to be centered around a certain value that can be called a central value. In this case, the measures of central tendency are the function used to recognize this central value to represent the data. In some cases, the data are closing to the central value and sometimes more widespread. In order to measure the proximity or distance of data from that central value, measures of  dispersion are used. In this research, some of these measures are used to find the basic acceptable solution for transportation problems. Better results are obtained by the researcher through using these measures by taking five applications with different capacities.
 

          There are many statistical methods related to the forecasting of time series without any input variables such as autoregressive integrated moving average (ARIMA models). In this research, some linear dynamic systems, represented by ARIMA with exogenous input variables (ARIMAX models) were used to forecast crude oil prices (considered as output variable) for OPEC organization with the help of crude oil production (considered as input variable) depending on the data starting from the period of 1973 until 2018. Using traditional ARIMAX method and proposed method (Bivariate Wavelet Filtering) for the time series data in order to select one of them for forecasting through comparing some measures of accuracy, such as MSE, FPE, and AIC. Then, applying crude oil prices for OPEC using the traditional ARIMAX models and ARIMAX models with applying the bivariate wavelet filtering, especially bivariate Haar wavelet. The main conclusions of the research were that the success of bivariate wavelet filtering in forecasting of crude oil prices using proposed model was more appropriate than traditional models, and the forecasting of crude oil prices using proposed method in 2020 will be fairly less than 2019.