2025
Joint modeling of mixed outcomes using a rank-based sparse neural network
Xue J, Xu Y, Li J, Ma S, Fang K. Joint modeling of mixed outcomes using a rank-based sparse neural network. Journal Of Biomedical Informatics 2025, 169: 104870. PMID: 40623577, PMCID: PMC12306493, DOI: 10.1016/j.jbi.2025.104870.Peer-Reviewed Original ResearchSparse neural networksNeural networkCompetitive performanceImbalance issueLoss functionSparse layerLeverage informationPrediction accuracyTraditional methodsNetworkParametric frameworkPenalization methodFaces challengesJoint modelPrediction modelInformationSkin cutaneous melanomaHigh-throughput profilingHigh-dimensional covariatesDimensionalityGenomic researchFeaturesMethodSimulation studyBiomedical studiesIntegrative rank-based regression for multi-source high-dimensional data with multi-type responses
Xu F, Ma S, Zhang Q. Integrative rank-based regression for multi-source high-dimensional data with multi-type responses. Journal Of Applied Statistics 2025, 52: 2011-2030. PMID: 40904949, PMCID: PMC12404076, DOI: 10.1080/02664763.2025.2452964.Peer-Reviewed Original Research
2024
The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies
Liu Y, Ren J, Ma S, Wu C. The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies. Statistics In Medicine 2024, 43: 4928-4983. PMID: 39260448, PMCID: PMC11585335, DOI: 10.1002/sim.10196.Peer-Reviewed Original ResearchAsymmetric Laplace distributionSpike-and-slab LASSORobust variable selection methodHeavy-tailed errorsRobust variable selectionHeavy-tailed distributionsAnalysis of high-dimensional genomic dataHigh-dimensional genomic dataExpectation-maximizationComprehensive simulation studyVariable selection methodsLaplace distributionCoordinate descent frameworkPosterior modeCancer genomics studiesRobust likelihoodVariable selectionSparsity patternSimulation studyComputational advantagesQuantile regressionNonrobust oneSelf-adaptationLoss functionGenomic studies
2022
Network-adaptive robust penalized estimation of time-varying coefficient models with longitudinal data
Fang K, Fan X, Ma S, Zhang Q. Network-adaptive robust penalized estimation of time-varying coefficient models with longitudinal data. Journal Of Statistical Computation And Simulation 2022, 92: 3045-3065. DOI: 10.1080/00949655.2022.2055758.Peer-Reviewed Original ResearchTime-varying coefficient modelsLikelihood-based estimationPenalization approachCoefficient modelStatistical modelPractical problemsNovel penaltyConsistency propertiesPractical performanceLoss functionNumerical studyLongitudinal dataEstimationNetwork structureNetwork connectivityConnection measuresModelData analysisRobustnessSimulationsInterconnectionProblemCovariatesField
2019
Penalized Relative Error Estimation of a Partially Functional Linear Multiplicative Model
Zhang T, Huang Y, Zhang Q, Ma S, Ahmed S. Penalized Relative Error Estimation of a Partially Functional Linear Multiplicative Model. Contributions To Statistics 2019, 127-144. DOI: 10.1007/978-3-030-17519-1_10.Peer-Reviewed Original ResearchFinite sample performanceRelative error estimationTecator dataScalar responseLinear multiplicative modelsScalar variablesSample performanceFunctional predictorsError estimationBasis functionsMultiplicative modelConsistency propertiesLeast squaresLoss functionTrue structureRelative errorClassic methodsEstimationPenalizationModelFunctional dataSquaresSimulations
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