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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 221230 of 472 papers

TitleStatusHype
Global Censored Quantile Random Forest0
Generalized Bayesian Ensemble Survival Tree (GBEST) model0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records0
Continuous Risk Measures for Driving Support0
Assumption-Free Survival Analysis Under Local Smoothness Prior0
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy0
Gaussian Processes for Survival Analysis0
ICBM community cancer registry analysis: a focus on Non-Hodgkin Lymphoma cases in missileers0
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba0
Continuous and Discrete-Time Survival Prediction with Neural Networks0
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