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Survival Analysis

Survival Analysis is a branch of statistics focused on the study of time-to-event data, usually called survival times. This type of data appears in a wide range of applications such as failure times in mechanical systems, death times of patients in a clinical trial or duration of unemployment in a population. One of the main objectives of Survival Analysis is the estimation of the so-called survival function and the hazard function. If a random variable has density function $f$ and cumulative distribution function $F$, then its survival function $S$ is $1-F$, and its hazard $λ$ is $f/S$.

Source: Gaussian Processes for Survival Analysis

Image: Kvamme et al.

Papers

Showing 251260 of 472 papers

TitleStatusHype
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence0
The TruEnd-procedure: Treating trailing zero-valued balances in credit data0
Time-to-Event Prediction with Neural Networks and Cox Regression0
Too Sick for Working, or Sick of Working? Impact of Acute Health Shocks on Early Labour Market Exits0
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches0
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning0
Towards inferring network properties from epidemic data0
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation0
Towards simple time-to-event modeling: optimizing neural networks via rank regression0
On Training Survival Models with Scoring Rules0
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