Bayesian Survival Analysis.

By M. May. 2002

898 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “We use survival models [18, 30] to model user return and user activeness.”
Bayesian Semiparametric Modelling in Quantile Regression (2009, methodology): “For the survival data of section 3.3, which include right censored observations, we also illustrate with conditional predictive ordinate (CPO) plots (see, e.g. Ibrahim et al., 2001).”
Cubic rank transmuted distributions: inferential issues and applications (2017, background): “Following this idea, several authors have considered extensions of some common survival distributions: see [2,3].”
Maximum likelihood estimation in semiparametric regression models with censored data (2007, methodology): “Ibrahim et al. (2001) provided an excellent description of Bayesian methods for semiparametric models with censored data.”
Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist (2017, positive): “Third, they can use the results from a meta-analysis to define hyperparameter values for the prior, where multiple studies are combined to form information about the parameter (Ibrahim, Chen, & Sinha, 2005; Rietbergen et al., 2011).”
The Kumaraswamy-Log-Logistic Distribution (2012, methodology): “These data (Ibrahim et al., 2001) present the survival times, T , defined as the time until the patient’s death.”
Neural Survival Recommender (2017, background): “Survival analysis [26, 16] has its roots in clinical studies where one tries to estimate the probability of survival of a patient.”
A flexible model for survival data with a cure rate: a Bayesian approach (2011, background): “The data were taken from [12] (see also [14]).”
The Kumaraswamy Generalized Half-Normal Distribution for Skewed Positive Data (2012, background): “The data were collected by Ibrahim et al. (2001). The survival time X is defined as the time until the patient’s death.”