Featured Publications
Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition
Liu T, Yuan M, Zhao H. Characterizing Spatiotemporal Transcriptome of the Human Brain Via Low-Rank Tensor Decomposition. Statistics In Biosciences 2022, 14: 485-513. DOI: 10.1007/s12561-021-09331-5.Peer-Reviewed Original ResearchLow-rank tensor decompositionTensor decompositionPower iterationClassical principal component analysisStatistical performanceNumerical experimentsTensor unfoldingStatistical methodsGene expression dataEfficient algorithmData matrixExpression dataTensor principal componentsBrain expression dataPrincipal component analysisIterationDecompositionSpatiotemporal transcriptomeImplicit assumptionAlgorithmDynamicsTrajectoriesGuaranteesAssumptionSpatial patterns
2020
A Set of Efficient Methods to Generate High-Dimensional Binary Data With Specified Correlation Structures
Jiang W, Song S, Hou L, Zhao H. A Set of Efficient Methods to Generate High-Dimensional Binary Data With Specified Correlation Structures. The American Statistician 2020, 75: 310-322. DOI: 10.1080/00031305.2020.1816213.Peer-Reviewed Original ResearchHigh-dimensional binary dataCommon correlation structuresCorrelation structureTime complexityUnequal probabilityStatistical methodsGeneral correlation matricesCorrelated binary dataBinary dataQuadratic time complexityMonte Carlo methodCorrelation matrixData simulationLinear time complexityIncrease of dimensionCarlo methodData generationEfficient algorithmTime costValidity conditionsComplexity methodBinary variablesSimulation methodR packageAlgorithm
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 estimation
2016
Estimating a sparse reduction for general regression in high dimensions
Wang T, Chen M, Zhao H, Zhu L. Estimating a sparse reduction for general regression in high dimensions. Statistics And Computing 2016, 28: 33-46. DOI: 10.1007/s11222-016-9714-6.Peer-Reviewed Original Research