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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 371380 of 472 papers

TitleStatusHype
Enabling Counterfactual Survival Analysis with Balanced RepresentationsCode1
Targeting Learning: Robust Statistics for Reproducible Research0
A General Framework for Survival Analysis and Multi-State ModellingCode1
Secure and Differentially Private Bayesian Learning on Distributed Data0
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models0
A Deep Recurrent Survival Model for Unbiased RankingCode1
Survival Analysis Using a 5-Step Stratified Testing and Amalgamation Routine in Randomized Clinical TrialsCode0
Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours0
An RNN-Survival Model to Decide Email Send Times0
Variational Learning of Individual Survival DistributionsCode0
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