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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 6170 of 472 papers

TitleStatusHype
Enabling Counterfactual Survival Analysis with Balanced RepresentationsCode1
A General Framework for Survival Analysis and Multi-State ModellingCode1
A Deep Recurrent Survival Model for Unbiased RankingCode1
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing RisksCode1
Survival Cluster AnalysisCode1
Adversarial Time-to-Event ModelingCode1
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural NetworkCode1
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies0
Soft decision trees for survival analysis0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs0
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