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 391–400 of 472 papers

TitleStatusHype
A kernel log-rank test of independence for right-censored dataCode0
Survival and Neural Models for Private Equity Exit Prediction—0
Better Approximate Inference for Partial Likelihood Models with a Latent Structure—0
Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival AnalysisCode0
Continuous and Discrete-Time Survival Prediction with Neural Networks—0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
Variable Selection with Random Survival Forest and Bayesian Additive Regression Tree for Survival Data—0
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease—0
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data—0
Modeling Time to Open of Emails with a Latent State for User Engagement Level—0
Show:102550
← PrevPage 40 of 48Next →

No leaderboard results yet.