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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

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Showing 391–400 of 472 papers

TitleStatusHype
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches—0
The Brier Score under Administrative Censoring: Problems and SolutionsCode0
A kernel log-rank test of independence for right-censored dataCode0
Survival and Neural Models for Private Equity Exit Prediction—0
Better Approximate Inference for Partial Likelihood Models with a Latent Structure—0
Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival AnalysisCode0
Continuous and Discrete-Time Survival Prediction with Neural Networks—0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
Variable Selection with Random Survival Forest and Bayesian Additive Regression Tree for Survival Data—0
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease—0
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