2013
Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference
Li S, Mukherjee B, Batterman S, Ghosh M. Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference. Biometrics 2013, 69: 925-936. PMID: 24289144, PMCID: PMC4108592, DOI: 10.1111/biom.12102.Peer-Reviewed Original ResearchConceptsSemi-parametric Bayesian approachLikelihood-based approachRandom nuisance parametersTime series analysisFrequentist literatureNuisance parametersDirichlet processInferential issuesConditional likelihoodPosterior distributionRisk functionTime seriesBayesian workFrequentist approachCase-crossover designSimulation studyRestrictive assumptionsBayesian approachTime Series DataLikelihood formulationBayesian methodsEquivalent resultsBayesian analysisCase-crossoverBayesian framework
2008
Semiparametric Bayesian modeling of random genetic effects in family‐based association studies
Zhang L, Mukherjee B, Hu B, Moreno V, Cooney K. Semiparametric Bayesian modeling of random genetic effects in family‐based association studies. Statistics In Medicine 2008, 28: 113-139. PMID: 18792083, PMCID: PMC2684653, DOI: 10.1002/sim.3413.Peer-Reviewed Original ResearchConceptsRandom effects distributionRandom effects parametersBayesian approachProblem of estimating covarianceSensitive to parametric specificationSemiparametric Bayesian modelNonparametric Bayesian approachFixed covariate effectsFlexible Bayesian approachEffective distributionIntegrated likelihoodDirichlet processCovariate effectsNonparametric modelBayesian paradigmParametric specificationHierarchical Bayesian paradigmBayes methodologyInference problemSimulation studyRandom genetic effectsComputational advantagesCorrelation structureNumerical integration schemeTheoretical senseModeling 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
2005
Semiparametric Bayesian Analysis of Matched Case-Control Studies With Missing Exposure
Sinha S, Mukherjee B, Ghosh M, Mallick B, Carroll R. Semiparametric Bayesian Analysis of Matched Case-Control Studies With Missing Exposure. Journal Of The American Statistical Association 2005, 100: 591-601. DOI: 10.1198/016214504000001411.Peer-Reviewed Original Research
2004
Bayesian Semiparametric Modeling for Matched Case–Control Studies with Multiple Disease States
Sinha S, Mukherjee B, Ghosh M. Bayesian Semiparametric Modeling for Matched Case–Control Studies with Multiple Disease States. Biometrics 2004, 60: 41-49. PMID: 15032772, DOI: 10.1111/j.0006-341x.2004.00169.x.Peer-Reviewed Original ResearchConceptsSemiparametric Bayesian frameworkBayesian semiparametric modelSemiparametric modelDirichlet processStratum effectsConditional likelihoodProbability of disease developmentBayesian approachNumerical integration schemeBayesian frameworkSample sizeDirichletActual estimationMLEMissingnessMarkovIntegration schemeExposure distributionBayesianEstimationRegression modelsMultiple disease statesDistributionProbabilityDisease states