The effect of polluted samples on Bayesian Estimators of Burr type –XII distribution

Bayesian estimators may be affected by the polluted samples, because these samples can lead to the influence of the estimation methods in general and the Bayesian methods in particular, and thus the deviation of the values of the distribution parameter from their real values, and this leads to the divergence of the capabilities of the Bayes survival estimators from the real values. The results showed that the estimators of the parameters were affected by many factors (sample size, distribution parameter, number of outliers and the estimation method). Simulation experiment results also showed a difference in Mean Square Error (MSE) of the Bayes survival estimators for each different experiment. Bayesian methods can be compared with other estimation methods (Maximum likelihood Estimation (MLE), Moment estimation (MOM) and shrinkage method (SH)). Also, Bayesian methods can be used to estimate the survival function of other distributions (exponential, Gamma and mixed) to observe the estimation results with the presence of extreme values

Tuning parameter selectors for bridge penalty based on particle swarm optimization method

The bridge penalty is widely used as a penalty for selecting and shrinking predictors in regression models. Although its effectiveness is sensitive to the parameters you decide to use for shrinking and adjusting. The shrinkage and tuning parameters of the bridge penalty are chosen concurrently, and a continuous optimization process called particle swarm optimization is proposed as a means to do this. If implemented, the proposed method will greatly facilitate regression modeling with superior prediction performance. The results show that the proposed method is effective in comparison to other well-known methods, but this varies greatly depending on the simulation setup and the real data application