Failure Time Model

Failure time models, also known as survival analysis, predict the time until an event occurs, accounting for censored data (incomplete observations). Current research emphasizes developing more flexible and robust models, particularly using machine learning techniques like neural networks and gradient boosting, often incorporating rank-based regression to handle complex relationships and avoid restrictive distributional assumptions. These advancements are crucial for diverse applications, from predicting equipment failures and improving healthcare risk assessment (e.g., heart failure hospitalization) to ensuring the safety and reliability of autonomous vehicles by modeling their mean time between failures.