comparative‎ ‎study‎ ‎of‎ ‎an‎ ‎ensemble‎ ‎machine learning‎ ‎model versus‎ ‎maximum‎ ‎likelihood model‎ ‎to assess‎ ‎reliability measures in right censored data analysis

‎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.