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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 381390 of 472 papers

TitleStatusHype
Region-specific Risk Quantification for Interpretable Prognosis of COVID-19Code0
Multi-Source Survival Domain AdaptationCode0
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measuresCode0
Reinterpreting survival analysis in the universal approximator ageCode0
Research Reproducibility as a Survival AnalysisCode0
Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand ForecastingCode0
ALBRT: Cellular Composition Prediction in Routine Histology ImagesCode0
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency RatesCode0
Exploring the Wasserstein metric for survival analysisCode0
Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival AnalysisCode0
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