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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 401410 of 472 papers

TitleStatusHype
Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using EEG Data: A Dynamic Survival Analysis Framework with Competing RisksCode0
Nonlinear Semi-Parametric Models for Survival AnalysisCode0
Flexible Group Fairness Metrics for Survival AnalysisCode0
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risksCode0
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCTCode0
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival AnalysisCode0
DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural NetworksCode0
SAFE: A Neural Survival Analysis Model for Fraud Early DetectionCode0
SAVAE: Leveraging the variational Bayes autoencoder for survival analysisCode0
NSOTree: Neural Survival Oblique TreeCode0
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