Survival Function

Survival analysis aims to model the time until an event occurs, accounting for censoring (incomplete data). Current research focuses on improving prediction accuracy and fairness using diverse model architectures, including deep neural networks (like variational autoencoders and neural ODEs), ensemble methods (e.g., combined regression strategies), and novel approaches leveraging implicit survival functions or the Beran estimator. These advancements enhance the interpretability and robustness of survival models, with implications for various fields including healthcare, finance, and engineering, where accurate prediction of event times is crucial for decision-making.