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
2015
A Small-Sample Choice of the Tuning Parameter in Ridge Regression.
Boonstra P, Mukherjee B, Taylor J. A Small-Sample Choice of the Tuning Parameter in Ridge Regression. Statistica Sinica 2015, 25: 1185-1206. PMID: 26985140, PMCID: PMC4790465, DOI: 10.5705/ss.2013.284.Peer-Reviewed Original Research
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-PCRAddressing extrema and censoring in pollutant and exposure data using mixture of normal distributions
Li S, Batterman S, Su F, Mukherjee B. Addressing extrema and censoring in pollutant and exposure data using mixture of normal distributions. Atmospheric Environment 2013, 77: 464-473. PMID: 24348086, PMCID: PMC3857711, DOI: 10.1016/j.atmosenv.2013.05.004.Peer-Reviewed Original ResearchFinite mixture of normalsDirichlet process mixtureMixtures of normalsDirichlet process mixtures of normalsFinite mixtureHeavy tailsDirichlet process mixture methodsMethod detection limitsComprehensive simulation studyDistributions of VOC concentrationsProcess mixtureStandard model assumptionsPosterior distributionEmpirical densityNormal distributionSimulation studyGoodness-of-fit criteriaVolatile organic compoundsDensity estimationGoodness-of-fitDensity estimation methodCensoringConvergence issuesExposure dataEstimation method