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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 441450 of 472 papers

TitleStatusHype
Deep Neural Networks for Survival Analysis Based on a Multi-Task FrameworkCode0
Survival-Supervised Topic Modeling with Anchor Words: Characterizing Pancreatitis Outcomes0
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification0
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles0
Statistical Inference for Data-adaptive Doubly Robust Estimators with Survival Outcomes0
Differentially Private Regression for Discrete-Time Survival Analysis0
Machine Learning for Survival Analysis: A Survey0
iSurvive: An Interpretable, Event-time Prediction Model for mHealth0
Predicting Session Length in Media Streaming0
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modellingCode0
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