2015
Data-Adaptive Shrinkage via the Hyperpenalized EM Algorithm
Boonstra P, Taylor J, Mukherjee B. Data-Adaptive Shrinkage via the Hyperpenalized EM Algorithm. Statistics In Biosciences 2015, 7: 417-431. PMID: 26834856, PMCID: PMC4728141, DOI: 10.1007/s12561-015-9132-x.Peer-Reviewed Original Research
2014
Latent variable models for gene–environment interactions in longitudinal studies with multiple correlated exposures
Tao Y, Sánchez B, Mukherjee B. Latent variable models for gene–environment interactions in longitudinal studies with multiple correlated exposures. Statistics In Medicine 2014, 34: 1227-1241. PMID: 25545894, PMCID: PMC4355187, DOI: 10.1002/sim.6401.Peer-Reviewed Original ResearchMeSH KeywordsBiostatisticsChild, PreschoolComputer SimulationEnvironmental ExposureFemaleGene-Environment InteractionHemochromatosis ProteinHistocompatibility Antigens Class IHumansInfantInfant, NewbornLead PoisoningLongitudinal StudiesMembrane ProteinsMexicoModels, GeneticModels, StatisticalPolymorphism, Single NucleotidePregnancyPrenatal Exposure Delayed EffectsConceptsGene-environment interactionsOutcome measuresCohort studyHealth effects of environmental exposuresEnvironmental exposuresInvestigate health effectsGene-environment associationsEffects of environmental exposuresEarly life exposuresLV frameworkG x E effectsMultivariate exposuresGenotyped single nucleotide polymorphismsEffect modificationShrinkage estimatorsLife exposureExposure measurementsSingle nucleotide polymorphismsData-adaptive wayMultiple testingOutcome dataLongitudinal studyLongitudinal natureGenetic factorsNucleotide polymorphisms
2011
Testing Gene-Environment Interaction in Large-Scale Case-Control Association Studies: Possible Choices and Comparisons
Mukherjee B, Ahn J, Gruber S, Chatterjee N. Testing Gene-Environment Interaction in Large-Scale Case-Control Association Studies: Possible Choices and Comparisons. American Journal Of Epidemiology 2011, 175: 177-190. PMID: 22199027, PMCID: PMC3286201, DOI: 10.1093/aje/kwr367.Peer-Reviewed Original ResearchConceptsGene-environment independenceGene-environment interactionsCase-only methodTesting gene-environment interactionsCase-control testsExposure under studyCase-control association studyUnderlying populationCase-control methodCase-control analysisFraction of markersType I error propertiesGenome-wide scanClass of proceduresAssociation studiesData-adaptive wayComparative simulation studyLarge-scale studiesEmpirical-BayesIndependence assumptionFalse positivesPopulationReplication strategyHybrid methodIndependence
2008
Modeling Unobserved Sources of Heterogeneity in Animal Abundance Using a Dirichlet Process Prior
Dorazio R, Mukherjee B, Zhang L, Ghosh M, Jelks H, Jordan F. Modeling Unobserved Sources of Heterogeneity in Animal Abundance Using a Dirichlet Process Prior. Biometrics 2008, 64: 635-644. PMID: 17680831, DOI: 10.1111/j.1541-0420.2007.00873.x.Peer-Reviewed Original ResearchConceptsSampling locationsSampling protocolNatural populations of animalsPredictions of abundanceAbundance of animalsDistribution of abundanceEndangered fish speciesInduce spatial heterogeneityAnimal abundanceOkaloosa DartersPopulations of animalsUnsampled locationsFish speciesRemoval samplingSpatial heterogeneityAnalysis of countsAbundanceDirichlet processData-adaptive wayModel specificationSources of heterogeneitySpeciesParametric alternativesDartersParametric model