2024
On Optimality of Mallows Model Averaging
Peng J, Li Y, Yang Y. On Optimality of Mallows Model Averaging. Journal Of The American Statistical Association 2024, ahead-of-print: 1-12. DOI: 10.1080/01621459.2024.2402566.Peer-Reviewed Original ResearchNon-nested setAsymptotic optimalityModel selectionCp criterionMallows model averageCandidate modelsOptimal convex combinationMallows' Cp criterionMinimax adaptiveSampling inequalitiesMallows modelConvex combinationTheoretical findingsOptimal riskSupplementary materialsMA estimatesModel AveragingTheoretical justificationModel weightsAverage modelNested setMild conditionsMinimaxEstimationInequalityGroup Response‐Adaptive Randomization With Delayed and Missing Responses
Zhai G, Li Y, Zhang L, Hu F. Group Response‐Adaptive Randomization With Delayed and Missing Responses. Statistics In Medicine 2024, 43: 5047-5059. PMID: 39285137, DOI: 10.1002/sim.10220.Peer-Reviewed Original ResearchIncorporating prior information in gene expression network-based cancer heterogeneity analysis
Li R, Xu S, Li Y, Tang Z, Feng D, Cai J, Ma S. Incorporating prior information in gene expression network-based cancer heterogeneity analysis. Biostatistics 2024, kxae028. PMID: 39074174, DOI: 10.1093/biostatistics/kxae028.Peer-Reviewed Original ResearchIntegrative deep learning with prior assisted feature selection
Wang F, Jia K, Li Y. Integrative deep learning with prior assisted feature selection. Statistics In Medicine 2024, 43: 3792-3814. PMID: 38923006, DOI: 10.1002/sim.10148.Peer-Reviewed Original ResearchDeep learningLearning methodsPerformance of feature selectionCapability of deep learningDeep learning methodsEnsemble learning methodFeature selectionPrior informationLearningIntegrated analysisSkin cutaneous melanomaDatasetRedundancyMethodSimulation studySuperioritySelectionCapabilityInformationFrameworkSequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance
Yang H, Qin Y, Li Y, Hu F. Sequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance. Biometrics 2024, 80: ujae047. PMID: 38801258, DOI: 10.1093/biomtc/ujae047.Peer-Reviewed Original ResearchConceptsCovariate balanceMarginal imbalanceAdaptive randomization methodsTreatment effect estimatesSequential allocation schemeModified Mahalanobis distanceRandomization methodSimulation studyCovariate imbalanceRandom processTheoretical guaranteesCovariatesImbalance measureEffect estimatesMahalanobis distanceCurrent patientSample size differencesMarginal balanceAllocation schemeConvergenceData analysisSuperior performance
2023
Modifiable risk factors for esophageal cancer in endoscopic screening population: A modeling study
Zhang Q, Wang F, Feng H, Xing J, Zhu S, Zhang H, Li Y, Wei W, Zhang S. Modifiable risk factors for esophageal cancer in endoscopic screening population: A modeling study. Chinese Medical Journal 2023, 137: 350-352. PMID: 38030561, PMCID: PMC10836889, DOI: 10.1097/cm9.0000000000002878.Peer-Reviewed Original ResearchZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data
Li Y, Wu M, Ma S, Wu M. ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data. Genome Biology 2023, 24: 208. PMID: 37697330, PMCID: PMC10496184, DOI: 10.1186/s13059-023-03046-0.Peer-Reviewed Original ResearchConceptsSingle-cell transcriptomic dataCell heterogeneitySingle-cell RNA sequencing data analysisRNA sequencing data analysisCluster-specific genesGene selectionScRNA-seq datasetsSequencing data analysisNegative binomial mixture modelTranscriptomic dataCell lineagesCell typesBinomial mixture modelsBiological understandingBatch effectsDropout eventsLineagesGenesRaw countsCritical componentSelectionSystemic analysisBalancing covariates in multi-arm trials via adaptive randomization
Yang H, Qin Y, Wang F, Li Y, Hu F. Balancing covariates in multi-arm trials via adaptive randomization. Computational Statistics & Data Analysis 2023, 179: 107642. DOI: 10.1016/j.csda.2022.107642.Peer-Reviewed Original ResearchVisualization and assessment of model selection uncertainty
Qin Y, Wang L, Li Y, Li R. Visualization and assessment of model selection uncertainty. Computational Statistics & Data Analysis 2023, 178: 107598. DOI: 10.1016/j.csda.2022.107598.Peer-Reviewed Original Research
2022
Spatio-temporally smoothed deep survival neural network
Li Y, Liang D, Ma S, Ma C. Spatio-temporally smoothed deep survival neural network. Journal Of Biomedical Informatics 2022, 137: 104255. PMID: 36462600, PMCID: PMC9845179, DOI: 10.1016/j.jbi.2022.104255.Peer-Reviewed Original ResearchNetwork-based cancer heterogeneity analysis incorporating multi-view of prior information
Li Y, Xu S, Ma S, Wu M. Network-based cancer heterogeneity analysis incorporating multi-view of prior information. Bioinformatics 2022, 38: 2855-2862. PMID: 35561185, PMCID: PMC9113254, DOI: 10.1093/bioinformatics/btac183.Peer-Reviewed Original Research
2021
Confidence graphs for graphical model selection
Wang L, Qin Y, Li Y. Confidence graphs for graphical model selection. Statistics And Computing 2021, 31: 52. DOI: 10.1007/s11222-021-10027-5.Peer-Reviewed Original ResearchGraphical model selectionModel selection uncertaintyResidual bootstrap procedureTraditional confidence intervalsSelection uncertaintyModel selectionModel selection methodsBootstrap procedureTrue modelDependence structureSampling distributionGraphModel settingsGraphical modelsPopulation parametersSelection methodNumerical studyGraphical toolConfidence intervalsTesting for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates
Li Y, Ma W, Qin Y, Hu F. Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates. Statistical Methods In Medical Research 2021, 30: 2148-2164. PMID: 33899607, DOI: 10.1177/09622802211008206.Peer-Reviewed Original ResearchConceptsCovariate-adaptive randomizationCovariate-adaptive randomization methodsCovariate-adaptive randomized trialsValidity of statistical inferenceInflated type I errorLinear modelType I errorGeneralized linear modelAsymptotic distributionProposed adjustment methodAsymptotic resultsInferential propertiesTest statisticsAnti-conservativeStatistical inferenceContinuous responseUnadjusted testsNull hypothesisTheoretical findingsTreatment effectsInvalid testsRandomization methodNumerical studyCovariatesAdjustment method
2019
Statistical Inference for Covariate-Adaptive Randomization Procedures
Ma W, Qin Y, Li Y, Hu F. Statistical Inference for Covariate-Adaptive Randomization Procedures. Journal Of The American Statistical Association 2019, 115: 1488-1497. DOI: 10.1080/01621459.2019.1635483.Peer-Reviewed Original ResearchCovariate-adaptive randomizationCovariate-adaptiveCovariate-adaptive randomization proceduresProperties of statistical methodsTheoretical resultsRandomization procedureLinear model frameworkAsymptotic representationCoin designTheoretical propertiesStatistical inferenceCovariate informationSimulation studySequential randomizationCovariate balanceInference propertiesSupplementary materialsComplete randomizationGeneral theoryBalanced treatment groupsCovariatesInferenceStatistical methodsRerandomizationRandomization
2014
Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion
Li Y, Yu C, Qin Y, Wang L, Chen J, Yi D, Shia B, Ma S. Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion. Journal Of Statistical Computation And Simulation 2014, 85: 2582-2595. DOI: 10.1080/00949655.2014.899362.Peer-Reviewed Original Research