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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 421430 of 472 papers

TitleStatusHype
GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settingsCode0
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing RisksCode0
Cross-Validation Is All You Need: A Statistical Approach To Label Noise EstimationCode0
Optimal Sparse Survival TreesCode0
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in geneticsCode0
Copula Entropy based Variable Selection for Survival AnalysisCode0
Heterogeneous Datasets for Federated Survival Analysis SimulationCode0
Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)Code0
Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event DataCode0
Contextual Explanation NetworksCode0
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