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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 301–310 of 472 papers

TitleStatusHype
Federated Survival Analysis with Discrete-Time Cox Models—0
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis—0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications—0
Forecasting Automotive Supply Chain Shortfalls with Heterogeneous Time Series—0
Forecasting from Clinical Textual Time Series: Adaptations of the Encoder and Decoder Language Model Families—0
FPBoost: Fully Parametric Gradient Boosting for Survival Analysis—0
From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning—0
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba—0
Gaussian Processes for Survival Analysis—0
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy—0
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