2015
Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth–death processes
Crawford FW, Weiss RE, Suchard MA. Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth–death processes. The Annals Of Applied Statistics 2015, 9: 572-596. PMID: 26500711, PMCID: PMC4617556, DOI: 10.1214/15-aoas809.Peer-Reviewed Original ResearchLongitudinal count dataImportant statistical problemSelf-reported countsBayesian mixture modelBirth-death processStatistical problemsBayesian hierarchical modelTrue distributionCount dataMixture modelInferential tasksHierarchical modelHeaping processParametersMultiplesModelInferenceEstimationDistributionGridProblemError
2006
Grid enabled magnetic resonance scanners for near real-time medical image processing
Crane J, Crawford F, Nelson S. Grid enabled magnetic resonance scanners for near real-time medical image processing. Journal Of Parallel And Distributed Computing 2006, 66: 1524-1533. DOI: 10.1016/j.jpdc.2006.03.009.Peer-Reviewed Original ResearchMedical image processingHPC resourcesImage processingHigh-performance computing gridsReal-time medical image processingGraphical software toolMedical imaging dataReal-time processingComputing GridImaging data setsPrototype applicationSoftware toolsParallel reconstructionProcessing jobsAcceptable timeData setsMultiple research groupsImaging dataGridProcessingHardwareInitial resultsResourcesScannerResearch groups