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
Improving estimation and prediction in linear regression incorporating external information from an established reduced model
Cheng W, Taylor J, Vokonas P, Park S, Mukherjee B. Improving estimation and prediction in linear regression incorporating external information from an established reduced model. Statistics In Medicine 2018, 37: 1515-1530. PMID: 29365342, PMCID: PMC5889759, DOI: 10.1002/sim.7600.Peer-Reviewed Original ResearchConceptsOutcome variable YEfficiency of estimationApproximate Bayesian inferenceBayes solutionVariable YNonlinear constraintsInferential frameworkVariable BE(Y|XImprove inferenceBayesian inferenceEffective computational methodParameter spaceReduced modelImproved estimatesLinear regression modelsTransformation approachStandard errorDunsonInferenceEstimationRegression modelsProblemCovariatesSpace