جستجوی مقالات مرتبط با کلیدواژه "شبیه سازی مونت کارلو" در نشریات گروه "آمار"

In this paper, for the first time, the upper truncated inverse Weibull (UTIW) distribution is proposed for modeling wind speed data.Since there is a upper limit for empirical wind speed data, this data can be represented by using the UTIW distribution. In this study, the UTIW distribution is introduced and some of its statistical properties are studied. Then, the parameters of this distribution are estimated by using different methods. Simulation studies for these estimators are presented. In addition, the mentioned distribution performance is tested on real wind speed data of Ardabil province in Iran. Based on the results of the analysis, it is found that the presented distribution in this study for modeling wind speed data is more appropriate than recently introduced distributions. Finally, this distribution can be used as an alternative model for evaluating wind speed data.

‎The estimation of R=P(X<Y) in the case that X and Y are two independent Kumaraswamy distributed random variables with different parameters for progressively Type-II censored samples is studied‎. ‎First assuming the same second shape parameters of two distributions‎, ‎the maximum likelihood estimation and different confidence intervals are considered‎. ‎Moreover‎, ‎in case the second shape parameters of two distributions are known‎, ‎MLE‎, ‎UMVUE‎, ‎Bayes estimation of R and confidence intervals are derived‎. ‎Finally‎, ‎the Maximum Likelihood and Bayes estimations of R in general case are obtained‎. ‎Performance comparisons of different methods are carried out utilizing Monte Carlo simulations‎. ‎Besides‎, ‎the proposed approach is employed for reliability analysis on a real strength-stress dataset to demonstrate its application‎.

In this article, we study parameter estimation of the logarithmic series distribution. A well-known method of estimation is the maximum likelihood estimate (MLE) and this method for this distribution resulted in a biased estimator for the small sample size datasets. The goal here is to reduce the bias and root mean square error of MLE of the unknown parameter. Employing the Cox and Snell method, a closed-form expression for the bias-reduction of the maximum likelihood estimator of the parameter is obtained. Moreover, the parametric Bootstrap bias correction of the maximum likelihood estimator is studied. The performance of the proposed estimators is investigated via Monte Carlo simulation studies. The numerical results show that the analytical bias-corrected estimator performs better than bootstrapped-based estimator and MLE for small sample sizes. Also, certain useful characterizations of this distribution are presented. An example via a real dataset is presented for the illustrative purposes.

In this paper, we consider a new class of bivariate copulas and study their measures of association. Specifically, we propose a bivariate copula based distribution and obtain explicit expressions for the corresponding marginal and joint distributions of concomitants of generalized order statistics. Using these results, we provide the minimum variance linear unbiased estimator for the location and scale parameters of the concomitants of order statistics of Burr and logistic distributions. Then, we introduce a class of absolutely continuous bivariate distributions whose univariate margins are exponential distributions. In addition, we discuss their properties such as moment generating function, stress-strength probability and reliability of two component systems. Monte Carlo simulations are performed to highlight properties of the parameters estimates. Finally, we analyze two data sets to illustrate the flexibility and potential of the proposed distribution compared to several competing models.

In this paper, we discuss the statistical inference on the unknown parameters and reliability function of type-II extreme value (EVII) distribution when the observed data are progressively type-II censored. By applying EM algorithm, we obtain maximum likelihood estimates (MLEs). We also suggest approximate maximum likelihood estimators (AMLEs), which have explicit expressions. We provide Bayes estimates using both the symmetric and asymmetric loss functions via squared error loss, LINEX loss, and general entropy loss functions. Bayes estimates are obtained using the idea of Lindley and Markov chain Monte Carlo techniques. Finally, Monte Carlo simulations are presented to illustrate the methods discussed in this paper. Analysis is also carried out for a real data set.

In this paper، we discuss different predictors of times to failure of units censored in a hybrid censored sample from exponential distribution. Bayesian and non-Bayesian point predictors for the times to failure of units are obtained. Non-Bayesian prediction intervals are obtained based on pivotal and highest conditional density methods. Bayesian prediction intervals are also proposed. One real data set has been analyzed to illustrate all the prediction methods. Finally، different prediction methods have been compared using Monte Carlo simulations.