SOTAVerified

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 301310 of 472 papers

TitleStatusHype
Survival Analysis on Structured Data using Deep Reinforcement Learning0
Hazard Gradient Penalty for Survival Analysis0
Flexible Group Fairness Metrics for Survival AnalysisCode0
Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis0
Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture0
Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks0
Calibration Error for Heterogeneous Treatment EffectsCode0
SimHawNet: A Modified Hawkes Process for Temporal Network SimulationCode0
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction modelsCode0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications0
Show:102550
← PrevPage 31 of 48Next →

No leaderboard results yet.