Featured Publications
Additive Conditional Independence for Large and Complex Biological Structures
Lee K, Li B, Zhao H. Additive Conditional Independence for Large and Complex Biological Structures. Springer Handbooks Of Computational Statistics 2022, 153-171. DOI: 10.1007/978-3-662-65902-1_8.Peer-Reviewed Original ResearchNonparametric Functional Graphical Modeling Through Functional Additive Regression Operator
Lee K, Li L, Li B, Zhao H. Nonparametric Functional Graphical Modeling Through Functional Additive Regression Operator. Journal Of The American Statistical Association 2022, 118: 1718-1732. DOI: 10.1080/01621459.2021.2006667.Peer-Reviewed Original ResearchRegression operatorMultivariate random functionsGraphical modelsOne-dimensional kernelCurse of dimensionalityRandom variablesStatistical objectsRandom functionExisting graphical modelsError boundsEstimation consistencyLarge-scale networksDistributional assumptionsGaussian distributionNonparametric approachNonlinear relationOperatorsGraphical modelingOperator levelNeighborhood selectionExponential rateSupplementary materialElectroencephalography datasetAssumptionDifferent nodesConditional Functional Graphical Models
Lee K, Ji D, Li L, Constable T, Zhao H. Conditional Functional Graphical Models. Journal Of The American Statistical Association 2021, 118: 257-271. PMID: 37193511, PMCID: PMC10181795, DOI: 10.1080/01621459.2021.1924178.Peer-Reviewed Original ResearchMultivariate random functionsFunctional graphical modelNew linear operatorMultivariate functional dataGraph structureConditional graphical modelGraphical modelsLinear operatorsGraphical modelingRandom functionPrecision matrixPrecision operatorCorresponding estimatorsUniform convergenceConditional graphFunctional settingGraph sizeCorrelation operatorPartial correlation matrixNonzero elementsConditioning setCorrelation matrixOperatorsGraphBrain functional connectivity networks
2017
On Joint Estimation of Gaussian Graphical Models for Spatial and Temporal Data
Lin Z, Wang T, Yang C, Zhao H. On Joint Estimation of Gaussian Graphical Models for Spatial and Temporal Data. Biometrics 2017, 73: 769-779. PMID: 28099997, PMCID: PMC5515703, DOI: 10.1111/biom.12650.Peer-Reviewed Original ResearchConceptsGaussian graphical modelsTemporal dataGraphical modelsComplex data structuresJoint estimationMarkov random field modelRandom field modelParallel computingSelection consistencyData structureStatistical inferenceNeighborhood selection methodTemporal dependenciesEfficient algorithmIndividual networksMultiple groupsSpatial dataModel convergesNetwork estimationField modelSelection methodNetworkPosterior probabilitySimulation studyImproved estimationGraphical model selection with latent variables
Wu C, Zhao H, Fang H, Deng M. Graphical model selection with latent variables. Electronic Journal Of Statistics 2017, 11: 3485-3521. DOI: 10.1214/17-ejs1331.Peer-Reviewed Original ResearchGraphical model selectionModel selection consistencyEfficient ADMM algorithmSparse precision matrixGraphical modelsGaussian graphical modelsGenetical genomics dataSelection consistencyPenalized estimationStatistical inferencePrecision matrixLatent variablesParameter estimationTheoretical propertiesIdentifiability conditionsADMM algorithmModel selectionSimulation studyConditional dependenceEstimationTrace lossSuperior performanceEstimatorGraphVariables
2016
On an additive partial correlation operator and nonparametric estimation of graphical models
Lee KY, Li B, Zhao H. On an additive partial correlation operator and nonparametric estimation of graphical models. Biometrika 2016, 103: 513-530. PMID: 29422689, PMCID: PMC5793672, DOI: 10.1093/biomet/asw028.Peer-Reviewed Original Research