فهرست نشریات علمی دسترسی آزاد ایران

‎This paper explores the estimation of a new power function under Type-II right censoring using two methods‎: ‎maximum likelihood estimation (MLE) and an ensemble machine learning model based on stacking‎. ‎The study aims to assess both methods' effectiveness in estimating various reliability measures‎, ‎such as hazard rate‎, ‎mean residual life‎, ‎variance residual life‎, ‎mean inactivity time‎, ‎and variance inactivity time‎. ‎The stacking model integrates five base models‎, ‎radial basis function neural network‎, ‎random forest‎, ‎Support Vector Regression (SVR)‎, ‎Multilayer Perceptron (MLP)‎, ‎and gradient boosting regression trees‎, ‎with an radial basis function neural network serving as a meta-learner for final predictions‎. ‎Numerical experiments compare the performance of the stacking model against MLE for Type-II censored data‎. ‎Results indicate that the stacking model significantly enhances the accuracy of reliability measure predictions‎, ‎showcasing its potential as a robust tool for reliability analysis in the context of Type-II censoring‎.