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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 201210 of 472 papers

TitleStatusHype
FPBoost: Fully Parametric Gradient Boosting for Survival Analysis0
From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning0
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba0
Gaussian Processes for Survival Analysis0
Continuous and Discrete-Time Survival Prediction with Neural Networks0
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy0
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification0
A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records0
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction0
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