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 frameworkAddressing 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