2022
Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods
Du J, Boss J, Han P, Beesley L, Kleinsasser M, Goutman S, Batterman S, Feldman E, Mukherjee B. Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods. Journal Of Computational And Graphical Statistics 2022, 31: 1063-1075. PMID: 36644406, PMCID: PMC9838615, DOI: 10.1080/10618600.2022.2035739.Peer-Reviewed Original ResearchVariable selectionSimultaneous coefficient estimationPenalized regression methodsBinary outcome dataObjective functionR-package <i>Shrinkage penaltyGeneral classCyclic coordinate descentVariable selection algorithmCoefficient estimatesSupplementary materialsMethod to dataCoordinate descentMultiple imputationALS riskMultiply-imputedOutcome dataFunction formulationSelectivity propertiesSelection algorithmEstimationOptimization algorithmMissingnessBiomedical applications
2012
On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case‐control study of colorectal cancer
Ghosh M, Song J, Forster J, Mitra R, Mukherjee B. On the equivalence of posterior inference based on retrospective and prospective likelihoods: application to a case‐control study of colorectal cancer. Statistics In Medicine 2012, 31: 2196-2208. PMID: 22495822, DOI: 10.1002/sim.5358.Peer-Reviewed Original ResearchConceptsPosterior inferenceCase-control study of colorectal cancerOdds ratio parametersCategorical response dataBayesian analysis of dataStudy of colorectal cancerCase-control studyGeneral classProspective likelihoodSimulation studyCategorical responsesBayesian analysisColorectal cancerMatched case-control studyInferenceAnalysis of dataResponse dataPriorsRetrospective designRetrospective modelEquivalence