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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 411420 of 472 papers

TitleStatusHype
A kernel log-rank test of independence for right-censored dataCode0
Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic informationCode0
Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent DemandCode0
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy GuaranteeCode0
Examining marginal properness in the external validation of survival models with squared and logarithmic lossesCode0
Gradient Boosting Survival Tree with Applications in Credit ScoringCode0
A General Machine Learning Framework for Survival AnalysisCode0
SimHawNet: A Modified Hawkes Process for Temporal Network SimulationCode0
Differentially Private Distributed InferenceCode0
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction modelsCode0
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