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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 461470 of 472 papers

TitleStatusHype
Gaussian Processes for Survival Analysis0
Learning Genomic Representations to Predict Clinical Outcomes in CancerCode0
Deep Survival Analysis0
Multi-Organ Cancer Classification and Survival Analysis0
Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment0
Hazard function models to estimate mortality rates affecting fish populations with application to the sea mullet (Mugil cephalus) fishery on the Queensland coast (Australia)0
SGD with Variance Reduction beyond Empirical Risk Minimization0
Discrete Stochastic Models in Continuous Time for Ecology0
Kernel Machines for Current Status Data0
Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data0
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