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
2013
Bayesian semiparametric analysis for two-phase studies of gene-environment interaction
Ahn J, Mukherjee B, Gruber S, Ghosh M. Bayesian semiparametric analysis for two-phase studies of gene-environment interaction. The Annals Of Applied Statistics 2013, 7: 543-569. PMID: 24587840, PMCID: PMC3935248, DOI: 10.1214/12-aoas599.Peer-Reviewed Original ResearchBayesian variable selection algorithmTwo-phase sampling designGene-environment independencePseudo-likelihood methodJoint effects of genotypeGene-environment interactionsHigh-dimensional modelsWeighted likelihoodCase-control study of colorectal cancerJoint distributionHierarchical priorsSemiparametric analysisRetrospective likelihoodGenetic markersCovariate informationLikelihood methodSimulation studyStudy of gene-environment interactionsStudy of colorectal cancerVariable selection algorithmBayesian approachPhase I dataSub-sample of casesBayesian methodsBayesian analysis