2022
A Zero-Inflated Logistic Normal Multinomial Model for Extracting Microbial Compositions
Zeng Y, Pang D, Zhao H, Wang T. A Zero-Inflated Logistic Normal Multinomial Model for Extracting Microbial Compositions. Journal Of The American Statistical Association 2022, 118: 2356-2369. DOI: 10.1080/01621459.2022.2044827.Peer-Reviewed Original ResearchMaximum likelihood estimationEfficient iterative algorithmProbabilistic PCA modelsEmpirical Bayes approachApproximation estimatorVariational approximationExcessive zerosM-estimationAsymptotic normalityIterative algorithmLikelihood estimationBayes approachCount dataHigh dimensionalityRaw count dataMultinomial modelExtensive simulationsZerosSupplementary materialMicrobiome dataCompositional natureEstimationPCA modelComposition estimationApproximation
2021
Statistical Methods for Analyzing Tree-Structured Microbiome Data
Wang T, Zhao H. Statistical Methods for Analyzing Tree-Structured Microbiome Data. Frontiers In Probability And The Statistical Sciences 2021, 193-220. DOI: 10.1007/978-3-030-73351-3_8.Peer-Reviewed Original ResearchStatistical methodsOnly relative informationMicrobiome data analysisMicrobiome dataEmpirical Bayes estimationCompositional predictorsBayes estimationComputational challengesRelative informationDimension reductionAbundance matrixTaxa countsMultinomial modelMicrobiome datasetsPhylogenetic informationMicrobial taxaPhylogenetic treeSequencing technologiesOriginal ecosystemMicrobial compositionOrders of magnitudeMatrixExperimental methodsLibrary sizeZeros
2019
Prediction Analysis for Microbiome Sequencing Data
Wang T, Yang C, Zhao H. Prediction Analysis for Microbiome Sequencing Data. Biometrics 2019, 75: 875-884. PMID: 30994187, DOI: 10.1111/biom.13061.Peer-Reviewed Original ResearchConceptsMonte Carlo expectation-maximization algorithmInverse regression modelReal data exampleTypes of covariatesNew statistical frameworkMaximum likelihood estimationExpectation-maximization algorithmDimension reduction structureInverse regressionStatistical frameworkData examplesStatistical challengesLikelihood estimationMicrobiome sequencing dataHuman microbiome studiesHuman microbiome compositionDifferent library sizesZerosPredictive analysisModelEstimationAlgorithmSimulationsRegression modelsFramework
2018
Models for Analyzing Zero-Inflated and Overdispersed Count Data: An Application to Cigarette and Marijuana Use
Pittman B, Buta E, Krishnan-Sarin S, O’Malley S, Liss T, Gueorguieva R. Models for Analyzing Zero-Inflated and Overdispersed Count Data: An Application to Cigarette and Marijuana Use. Nicotine & Tobacco Research 2018, 22: 1390-1398. PMID: 29912423, PMCID: PMC7364829, DOI: 10.1093/ntr/nty072.Peer-Reviewed Original ResearchZero-inflated negative binomialZero-inflationZero-inflated PoissonNegative binomialAbundance of zerosCount dataIllustrative data exampleCount outcomesHurdle PoissonData examplesZINB modelFit statisticsPoissonLarge positive skewModel fitZerosNB modelBinomialAppropriate modelHurdle modelLarge varianceSpurious resultsModelOverdispersionSuch data
2016
Statistical Models for the Analysis of Zero-Inflated Pain Intensity Numeric Rating Scale Data
Goulet JL, Buta E, Bathulapalli H, Gueorguieva R, Brandt CA. Statistical Models for the Analysis of Zero-Inflated Pain Intensity Numeric Rating Scale Data. Journal Of Pain 2016, 18: 340-348. PMID: 27919777, DOI: 10.1016/j.jpain.2016.11.008.Peer-Reviewed Original ResearchConceptsStatistical modelStatistical methodsExcess of zerosAlternative statistical methodsFollowing statistical modelsNumeric rating scaleNRS scoresDiscrete valuesOrdinal dataLarge cohortLinear modelCumulative logit modelMusculoskeletal disordersRight-skewed distributionZerosObservational cross-sectional studyInterpretability of resultsMean NRS painPainful musculoskeletal disordersPredictor effectsVeterans Affairs (VA) carePain intensity dataCross-sectional studyNRS dataDiagnosis date
2011
Bayesian Time-Series Analysis of a Repeated-Measures Poisson Outcome With Excess Zeroes
Murphy TE, Van Ness PH, Araujo KL, Pisani MA. Bayesian Time-Series Analysis of a Repeated-Measures Poisson Outcome With Excess Zeroes. American Journal Of Epidemiology 2011, 174: 1230-1237. PMID: 22025357, PMCID: PMC3254157, DOI: 10.1093/aje/kwr252.Peer-Reviewed Original ResearchConceptsPosterior predictive simulationsExcess zerosBayesian modelBayesian time series analysisPredictive simulationsHierarchical Bayesian modelPoisson outcomesPosterior distributionTime series analysisBayesian frameworkRelated resultsStatistical factorsBayesian analysisRandom effects Poisson modelFrequentistZerosPoisson modelSmall samplesExcessive numberAutocorrelationSimulationsTime series techniquesModelPeriodicity
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