Right Censored

Right censoring, where the exact time of an event is unknown because it occurs after the observation period ends, is a common challenge in survival analysis. Current research focuses on developing and comparing robust methods for analyzing right-censored data, including classical approaches like Cox proportional hazards models and more modern machine learning techniques such as Bayesian neural networks and survival forests. These efforts aim to improve the accuracy and efficiency of predictions in various applications, from predictive maintenance to healthcare, where incomplete event data is prevalent. The development of improved methods for handling right-censored data is crucial for reliable inference and decision-making in numerous fields.