2025
Probabilistic exponential family inverse regression and its applications
Pang D, Zhu R, Zhao H, Wang T. Probabilistic exponential family inverse regression and its applications. Biometrics 2025, 81: ujaf065. PMID: 40407023, DOI: 10.1093/biomtc/ujaf065.Peer-Reviewed Original ResearchConceptsExponential familyDouble exponential familyHigh-dimensional regressionLow-dimensional reductionHierarchical likelihoodData exampleInverse regressionDiscrete predictorsSimulation studyDiscrete dataHigh-dimensional dataParallelizable algorithmContinuous predictorsPresence–absence recordsDimension reductionResponse variablesAccumulation of high dimensional dataHigh-throughput sequencing technologyFactor model frameworkLatent factorsRecords of speciesSequence readsSingle-cell studiesSequencing technologiesCommunity ecology
2023
Factor Augmented Inverse Regression and its Application to Microbiome Data Analysis
Pang D, Zhao H, Wang T. Factor Augmented Inverse Regression and its Application to Microbiome Data Analysis. Journal Of The American Statistical Association 2023, 119: 1957-1967. DOI: 10.1080/01621459.2023.2231577.Peer-Reviewed Original ResearchInverse regressionCount vectorsLow-dimensional summariesAsymptotic propertiesVariational expectation-maximization algorithmExpectation-maximization algorithmGroup lassoVariational approximationApproximate inferenceSupplementary materialsCount dataModel selectionInformation criterionAbundance of featuresVectorPrediction of host phenotypeOverdispersionLatent factorsApproximation
2013
Application of Bayesian Sparse Factor Analysis Models in Bioinformatics
Ma H, Zhao H. Application of Bayesian Sparse Factor Analysis Models in Bioinformatics. 2013, 350-365. DOI: 10.1017/cbo9781139226448.018.Peer-Reviewed Original ResearchFactor analysis modelClassical factor analysis modelLatent variable modelStatistical methodsInferential methodsVariable modelComputational biologyLarge data setsGeometrical procedureObserved variablesCorrelated variablesAnalysis modelGeneral approachLatent variablesFactor modelingLatent factorsStrong prior beliefsUnderlying structureData setsPrincipal component analysisModelVariablesRegulatory networksLarge numberPrior beliefs
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