Featured Publications
A meta-inference framework to integrate multiple external models into a current study.
Gu T, Taylor J, Mukherjee B. A meta-inference framework to integrate multiple external models into a current study. Biostatistics 2021, 24: 406-424. PMID: 34269371, PMCID: PMC10102901, DOI: 10.1093/biostatistics/kxab017.Peer-Reviewed Original ResearchConceptsAccuracy of statistical inferenceEmpirical Bayes estimatorsSummary-level informationBias-variance trade-offRelevant external informationBayes estimatorsStatistical inferenceExternal informationExternal estimatesNaive analysisNaive combinationInternational dataWeight estimationExternal modelMeta-analysis frameworkIndividual-level dataEfficiency gainsEstimationInfluence of informationTrade-offsInformationFramework
2018
Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability
Estes J, Mukherjee B, Taylor J. Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability. Statistics In Biosciences 2018, 10: 568-586. PMID: 31123532, PMCID: PMC6529204, DOI: 10.1007/s12561-018-9217-4.Peer-Reviewed Original ResearchEmpirical Bayes estimatorsSummary-level informationConstrained maximum likelihoodBayes estimatorsEmpirical Bayes shrinkage estimatorsSimulation studyBayes shrinkage estimatorShrinkage estimatorsLikelihood estimationCovariate distributionsConditional probability distributionData applicationsTrade biasMaximum likelihoodProbability distributionLoss of efficiencyCancer Prevention TrialIndividual-level dataEstimationProstate Cancer Prevention TrialPrevention trialsInternational population
2017
Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies
Estes J, Rice J, Li S, Stringham H, Boehnke M, Mukherjee B. Meta‐analysis of gene‐environment interaction exploiting gene‐environment independence across multiple case‐control studies. Statistics In Medicine 2017, 36: 3895-3909. PMID: 28744888, PMCID: PMC5624850, DOI: 10.1002/sim.7398.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsAlpha-Ketoglutarate-Dependent Dioxygenase FTOBayes TheoremBiasBiometryBody Mass IndexCase-Control StudiesComputer SimulationDiabetes Mellitus, Type 2Gene-Environment InteractionHumansLogistic ModelsMeta-Analysis as TopicModels, GeneticModels, StatisticalPolymorphism, Single NucleotideRetrospective StudiesConceptsGene-environment independenceGene-environmentEmpirical Bayes estimatorsGene-environment interactionsCase-control studyMeta-analysis settingBayes estimatorsRetrospective likelihood frameworkShrinkage estimatorsMeta-analysisTesting gene-environment interactionsCombination of estimatesFactors body mass indexSimulation studyBody mass indexUnconstrained modelLikelihood frameworkInverse varianceMeta-analysis frameworkFTO geneMass indexGenetic markersEstimationStandard alternativeChatterjee
2005
Nonparametric Sequential Bayes Estimation of the Distribution Function
Ghosh M, Mukherjee B. Nonparametric Sequential Bayes Estimation of the Distribution Function. Sequential Analysis 2005, 24: 389-409. DOI: 10.1080/07474940500311013.Peer-Reviewed Original Research