2024
Improving prediction of linear regression models by integrating external information from heterogeneous populations: James–Stein estimators
Han P, Li H, Park S, Mukherjee B, Taylor J. Improving prediction of linear regression models by integrating external information from heterogeneous populations: James–Stein estimators. Biometrics 2024, 80: ujae072. PMID: 39101548, PMCID: PMC11299067, DOI: 10.1093/biomtc/ujae072.Peer-Reviewed Original ResearchConceptsJames-Stein estimatorLinear regression modelsIndividual-level dataComprehensive simulation studyRegression modelsNumerical performanceSimulation studyShrinkage methodCoefficient estimatesPredictive meanReduced modelStudy population heterogeneityInternal modelEstimationStudy populationBlood lead levelsInternational studiesCovariatesPatella bonePublished literatureLead levelsExternal studiesSummary informationPopulationSubsets
2018
Distributed Lag Interaction Models with Two Pollutants
Chen Y, Mukherjee B, Berrocal V. Distributed Lag Interaction Models with Two Pollutants. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2018, 68: 79-97. PMID: 30636815, PMCID: PMC6328049, DOI: 10.1111/rssc.12297.Peer-Reviewed Original ResearchMean square errorEffects of air pollutionDistributed lag modelsAir pollution studiesHealth outcomesNational MorbidityBias-variance tradeoffEnvironmental epidemiologyAir pollutionPollution studiesPollutionLag effectMortality countsMain effectTensor productShrinkage methodShrinkage versionAverage performanceNatural waySimulation studyJoint effectsInteraction structureMortalityNMMAPSMorbidity
2017
Robust distributed lag models using data adaptive shrinkage
Chen Y, Mukherjee B, Adar S, Berrocal V, Coull B. Robust distributed lag models using data adaptive shrinkage. Biostatistics 2017, 19: 461-478. PMID: 29040386, PMCID: PMC6454578, DOI: 10.1093/biostatistics/kxx041.Peer-Reviewed Original ResearchConceptsDistributed lag modelsDistributed LagLag modelTime series dataEffects of air pollutionBias-variance trade-offGeneralized ridge regressionShrinkage methodAir pollution studiesHierarchical Bayes approachShrinkage approachTime seriesDl functionAir pollutionPollution studiesEffect estimatesTrade-offsExtensive simulation studyDependent variableShrinking coefficientsMean square errorLagSimulation studyBayes approachRidge regression
2013
Bayesian shrinkage methods for partially observed data with many predictors
Boonstra P, Mukherjee B, Taylor J. Bayesian shrinkage methods for partially observed data with many predictors. The Annals Of Applied Statistics 2013, 7: 2272-2292. PMID: 24436727, PMCID: PMC3891514, DOI: 10.1214/13-aoas668.Peer-Reviewed Original ResearchFraction of missing informationOptimal bias-variance tradeoffBayesian shrinkage methodsEmpirical Bayes algorithmComprehensive simulation studyBias-variance tradeoffSurrogate covariatesSimulation studyShrinkage methodCovariatesPrediction problemState-of-the-artModel parametersProblemMissing dataLung cancer datasetBayes algorithmState-of-the-art technologiesArray technologyCancer datasetsQRT-PCR