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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 331–340 of 472 papers

TitleStatusHype
Interpretable Prediction and Feature Selection for Survival Analysis—0
Interpretable Survival Analysis for Heart Failure Risk Prediction—0
Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps—0
iSurvive: An Interpretable, Event-time Prediction Model for mHealth—0
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data—0
KL-divergence Based Deep Learning for Discrete Time Model—0
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors—0
Learning Survival Distributions with the Asymmetric Laplace Distribution—0
Likelihood-Free Dynamical Survival Analysis Applied to the COVID-19 Epidemic in Ohio—0
Likelihood Ratio Confidence Sets for Sequential Decision Making—0
Show:102550
← PrevPage 34 of 48Next →

No leaderboard results yet.