In this paper a new method of estimation, named Adjusted Probability Weighted Moment (APWM), is introduced which is an extension of the Probability Weighted Moment (PWM). Based on APWM, a statistical inference is carried out on the parameter of Uniform distribution on (0, q). The results are compared with the ones of the other methods like the Maximum Likelihood (ML) and PWM. The APWM outperforms the PWM and gives the results at least similar to ML.

This paper proposed a new method for parameters estimation of the Ornstein Uhlenbeck processes driven with the compound Poisson process. These processes have some applications in modeling and forecasting in financial markets. The proposed estimators are derived based on the method of the moment. In this work, the central limit theorem for the proposed estimators is also established. Numerical experiments are provided to show that the proposed method performs better in comparison with the existing methods, especially in cases when the jumps of the compound Poisson process are relatively rare. As an experimental approach, we fit the Mobarakeh Steel company data with Gamma, Pareto, and Normal Ornstein Uhlenbeck processes and estimate the parameters by using the proposed method. Finally, under these stochastic models, we simulate the volatility of the Mobarakeh Steel Company.

In this paper, we first introduce semi-parametric heteroscedastic hierarchical models. Then, we define a new version of the empirical likelihood function (Restricted Joint Empirical likelihood) and use it to obtain the shrinkage estimators of the models' parameters in these models. Under different assumptions, a simulation study investigates the better performance of the restricted joint empirical likelihood function in the analysis of semi-parametric heterogeneity hierarchical models. Furthermore, we analyze an actual data set using the RJEL method.

In this paper, we show that the unbiased estimator of the certion Parameter of the selected population does not exist. First, we give a new proof of this fact for the selected normal population. We then extend the result to some other distributions belonging to one-parameteric exponential family. Whenever an unbiased estimator exists, it is shown to be a function of order statiscs.

In this paper we propose an estimator of the entropy of a continuous random variable. The estimator is obtained by modifying the estimator proposed by Vasicek (1976). Consistency of estimator is proved, and comparisons are made with Vasicek’s estimator (1976), van Es’s estimator (1992), Ebrahimi et al.’s estimator (1994) and Correa’s estimator (1995). The results indicate that the proposed estimator has smaller mean squared error than above estimators.

In this article new generalization of half-normal distribution as the half generalized normal distribution is introduced. ‎This distribution, contains the half-normal distribution as special case.‎ We provide mathematical properties of this distribution. We also derive the pdf, cdf, -th moment, the asymmetry and kurtosis coefficients and the moment generating function. We discuss some inferential aspects related to the maximum likelihood estimation. Finally we illustrate the flexibility of this type of distribution with applications to real data sets.

The choice of the best method estimation method is one of the goals in estimating parameters of the distribution. In this paper, I obtain the maximum likelihood and moment estimators of the two unknown parameters of a burr type XII distribution. Also Bayesian estimator of the parameter p under two different loss functions are derived. It is assumed that the parameter p behaves as a random variable has a gamma prior. Finally, numerical examples are given to illustrate some of theoretical results.

Due to the lack of measurement in distribution systems, state estimation has particular importance. Different methods are presented to improve the accuracy of system state with limited measurements. In this paper a new state estimator in distribution systems are offered. This estimator bases on backward forward load flow estimates system state with adjusting load consumption at each step. Voltage measurements in slack bus, loads and zero injection measurements are inputs of estimator. This estimator is compared with weight least square estimator and its results are shown. The estimator calculates voltage magnitude with less error and also faster than WLS estimator. 85-bus system is presented in this paper.