Bayesian thresholded modeling for integrating brain node and network predictors
Sun Z, Xu W, Li T, Kang J, Alanis-Lobato G, Zhao Y. Bayesian thresholded modeling for integrating brain node and network predictors. Biostatistics 2024, 26: kxae048. PMID: 39780514, PMCID: PMC11823287, DOI: 10.1093/biostatistics/kxae048.Peer-Reviewed Original ResearchConceptsPrediction mechanismNetwork-level metricsExtensive simulationsNetwork predictorPrior modelsSub-networksVector-variantPosterior inferenceNodesSignal patternsPredictable componentBrain nodesSpatial contiguityBayesian regression modelsImagesHierarchyLiterature gapNetworkMetricsCommunicationAlternative approachOut-of-sample predictionsInferenceModelBayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event
Tian X, Ciarleglio M, Cai J, Greene E, Esserman D, Li F, Zhao Y. Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2024, 73: 598-620. PMID: 39072299, PMCID: PMC11271983, DOI: 10.1093/jrsssc/qlae003.Peer-Reviewed Original ResearchSemi-parametric inferenceRecurrent eventsAccelerated failure time modelFailure time modelEfficient sampling algorithmFrailty distributionDirichlet processPosterior inferenceSampling algorithmTime modelTerminal eventSurvival processesComplex data structuresDirichletInferenceData structureFall injury preventionAlgorithm
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