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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 391400 of 472 papers

TitleStatusHype
The Brier Score under Administrative Censoring: Problems and SolutionsCode0
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional DataCode0
Neural Fine-Gray: Monotonic neural networks for competing risksCode0
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
Deep Clustering Survival Machines with Interpretable Expert DistributionsCode0
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariatesCode0
Feature Selection for Survival Analysis with Competing Risks using Deep LearningCode0
BoXHED2.0: Scalable boosting of dynamic survival analysisCode0
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features RelateCode0
Federated Survival ForestsCode0
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