2023
Nonparametric Statistical Inference via Metric Distribution Function in Metric Spaces
Wang X, Zhu J, Pan W, Zhu J, Zhang H. Nonparametric Statistical Inference via Metric Distribution Function in Metric Spaces. Journal Of The American Statistical Association 2023, ahead-of-print: 1-13. DOI: 10.1080/01621459.2023.2277417.Peer-Reviewed Original ResearchMetric spaceStatistical inferenceGlivenko-CantelliCorrespondence theoremEuclidean spaceGlivenko-Cantelli theoremDistribution functionNonparametric statistical inferenceDonsker propertyProbability measuresGeneral spaceRandom objectsTheoremMeasurement theorySupplementary materialsEuclideanSpaceIndependence testInferenceDonskerCorrespondenceNonparametricHypercubeMetricsFunction
2020
Relative Efficiency of Using Summary Versus Individual Data in Random-Effects Meta-Analysis
Chen D, Liu D, Min X, Zhang H. Relative Efficiency of Using Summary Versus Individual Data in Random-Effects Meta-Analysis. Biometrics 2020, 76: 1319-1329. PMID: 32056197, PMCID: PMC7955582, DOI: 10.1111/biom.13238.Peer-Reviewed Original ResearchConceptsMaximum likelihood estimationSummary statisticsAsymptotic senseStatistical methodologyLikelihood estimationGaussian distributionInference settingHeterogeneity parametersRelative efficiencyRandom effectsSample sizeStatisticsInferenceData setsModelEfficient conclusionsEstimationIndividual participant dataAssumptionParametersEfficiency
2018
Modeling hybrid traits for comorbidity and genetic studies of alcohol and nicotine co-dependence
Zhang H, Liu D, Zhao J, Bi X. Modeling hybrid traits for comorbidity and genetic studies of alcohol and nicotine co-dependence. The Annals Of Applied Statistics 2018, 12: 2359-2378. PMID: 30666272, PMCID: PMC6338437, DOI: 10.1214/18-aoas1156.Peer-Reviewed Original ResearchFull likelihoodComplicated likelihood functionsParametric frameworkParametric modeling frameworkHigh computational complexityExtensive simulation studyModel fitting algorithmStatistical inferenceRank-based methodLikelihood functionComputational burdenType I error ratesComputational complexityFitting algorithmSimulation studyNovel multivariate modelModeling frameworkEffect size estimationLatent variablesHybrid modelInferenceSize estimationModelFrameworkTheory
2016
Multiple Change-Point Detection: A Selective Overview
Niu Y, Hao N, Zhang H. Multiple Change-Point Detection: A Selective Overview. Statistical Science 2016, 31: 611-623. DOI: 10.1214/16-sts587.Peer-Reviewed Original Research