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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 331340 of 472 papers

TitleStatusHype
Exploring the Wasserstein metric for survival analysisCode0
The Expediting Effect of Monitoring on Infrastructural Works. A Regression-Discontinuity Approach with Multiple Assignment Variables0
Variational Bayes survival analysis for unemployment modelling0
Computing the Hazard Ratios Associated with Explanatory Variables Using Machine Learning Models of Survival DataCode0
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting0
Dynamic prediction of time to event with survival curves0
CDS -- Causal Inference with Deep Survival Model and Time-varying CovariatesCode1
Deep Cox Mixtures for Survival RegressionCode1
X-CAL: Explicit Calibration for Survival AnalysisCode1
BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values0
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