Yuan Huang, PhD
Assistant Professor of Biostatistics (Biostatistics)Cards
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Assistant Professor of Biostatistics (Biostatistics)
Biography
Yuan Huang is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health. Her methodological research is focused on statistical methods for high-dimensional data and has been motivated by challenges posted by analyzing cancer genomics, such as low reproducibly, nonlinearity, and heterogeneity. Applications from her work include biomarker identification, large-scale network structure estimation, GxE analysis, etc. She is particularly interested in integrative analysis that simultaneously analyzes multiple datasets to improve the discovery. Recently she collaborates extensively in neurodegenerative diseases and movement disorders, such as Alzheimer's disease, Huntington’s disease, and Parkinson's disease . She is also actively involved in collaborative research on clinical trials, genetics, epidemiology, and other biomedical fields.
Appointments
Biostatistics
Assistant ProfessorPrimary
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Education & Training
- PhD
- The Pennsylvania State University, Statistics
Research
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Public Health Interests
ORCID
0000-0002-8011-3034
Publications
2021
Bayesian finite mixture of regression analysis for cancer based on histopathological imaging-environment interactions.
Im Y, Huang Y, Tan A, Ma S. Bayesian finite mixture of regression analysis for cancer based on histopathological imaging-environment interactions. Biostatistics 2021, 24: 425-442. PMID: 37057611, PMCID: PMC10102889, DOI: 10.1093/biostatistics/kxab038.Peer-Reviewed Original ResearchCitationsAltmetricPromote sign consistency in the joint estimation of precision matrices
Zhang Q, Ma S, Huang Y. Promote sign consistency in the joint estimation of precision matrices. Computational Statistics & Data Analysis 2021, 159: 107210. DOI: 10.1016/j.csda.2021.107210.Peer-Reviewed Original ResearchCitationsConceptsMultiple precision matricesPrecision matrixRegularization methodJoint estimationGroup parametersSign consistencyConsistency propertiesGaussian graphical modelsNovel regularization methodHigh-dimensional dataRandom variablesSparsity structureData examplesMore interpretable resultsNatural interpretationConditional independenceInterpretable resultsGraphical modelsPractical examplesEstimationConflicting signsPopular toolMatrixParametersFull flexibility
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 ResearchCitationsConceptsFinite sample performanceRelative error estimationTecator dataScalar responseLinear multiplicative modelsScalar variablesSample performanceFunctional predictorsError estimationBasis functionsMultiplicative modelConsistency propertiesLeast squaresLoss functionTrue structureRelative errorClassic methodsEstimationPenalizationModelFunctional dataSquaresSimulations
2018
A Forward and Backward Stagewise algorithm for nonconvex loss functions with adaptive Lasso
Shi X, Huang Y, Huang J, Ma S. A Forward and Backward Stagewise algorithm for nonconvex loss functions with adaptive Lasso. Computational Statistics & Data Analysis 2018, 124: 235-251. PMID: 30319163, PMCID: PMC6181148, DOI: 10.1016/j.csda.2018.03.006.Peer-Reviewed Original ResearchCitationsConceptsNonconvex loss functionsHigh-dimensional settingsLoss functionConvex loss functionsAdaptive LASSO penaltySecond-order derivativesStagewise algorithmHigh-dimensional dataExtensive numerical studyApproximate solutionAdaptive lassoRank estimationLasso penaltyComputational algorithmStationary pointsEffective algorithmImportant applicationsNumerical studyAlgorithmPopular toolRobust resultsEstimationPenalizationProblemLASSO
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300 George Street
Academic Office
Fl 5th , Ste Ste 511
New Haven, CT 06511