hazard-survival-modeling

Discrete-time survival analysis for token sequences estimates catastrophe risk by modeling hazard probabilities at each token step, enabling reliable prediction of failure onset during sequential generation tasks.

You predict catastrophe onset by expanding token sequences into person-period data and fitting a binary classifier, such as XGBoost, to estimate hazard probabilities at each discrete time step.

Yes, XGBoost is used for hazard modeling by fitting a binary classifier on expanded person-period data, allowing you to compute calibrated survival curves and predict catastrophe onset across token horizons.

Horizon labeling strategies in survival analysis define target windows for catastrophe events across token sequences, allowing models to handle competing risks and structure discrete-time hazard prediction effectively.

Yes, the survival modeling approach handles censoring by applying discrete-time survival theory to token sequences, allowing you to estimate hazard rates even when catastrophe events are not fully observed.

Evaluation metrics for discrete-time hazard models assess predictive accuracy and calibration across survival curves, ensuring reliable catastrophe onset prediction within sequential token generation workflows.