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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 281290 of 472 papers

TitleStatusHype
Censored Quantile Regression Neural Networks for Distribution-Free Survival AnalysisCode1
Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis0
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask LearningCode1
PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing RisksCode1
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
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical DataCode1
SimHawNet: A Modified Hawkes Process for Temporal Network SimulationCode0
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction modelsCode0
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