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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 201210 of 472 papers

TitleStatusHype
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers0
Deep Learning for Survival Analysis: A ReviewCode1
Learning Survival Distribution with Implicit Survival FunctionCode0
survAIval: Survival Analysis with the Eyes of AI0
A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular InformationCode0
Neural Fine-Gray: Monotonic neural networks for competing risksCode0
Proper Scoring Rules for Survival AnalysisCode0
Diffsurv: Differentiable sorting for censored time-to-event dataCode0
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions0
Using Geographic Location-based Public Health Features in Survival AnalysisCode0
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