2024
Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease
Lila E, Zhang W, Levendovszky S, Weiner M, Aisen P, Weiner M, Aisen P, Petersen R, Jack C, Jagust W, Trojanowki J, Toga A, Beckett L, Green R, Saykin A, Morris J, Perrin R, Shaw L, Khachaturian Z, Carrillo M, Potter W, Barnes L, Bernard M, Ho C, Hsiao J, Jackson J, Masliah E, Masterman D, Okonkwo O, Perrin R, Ryan L, Silverberg N, Fleisher A, Weiner M, Fockler J, Conti C, Veitch D, Neuhaus J, Jin C, Nosheny R, Ashford M, Flenniken D, Kormos A, Green R, Montine T, Conti C, Petersen R, Aisen P, Rafii M, Raman R, Jimenez G, Donohue M, Gessert D, Salazar J, Zimmerman C, Cabrera Y, Walter S, Miller G, Coker G, Clanton T, Hergesheimer L, Smith S, Adegoke O, Mahboubi P, Moore S, Pizzola J, Shaffer E, Sloan B, Beckett L, Harvey D, Donohue M, Jack C, Forghanian-Arani A, Borowski B, Ward C, Schwarz C, Jones D, Gunter J, Kantarci K, Senjem M, Vemuri P, Reid R, Fox N, Malone I, Thompson P, Thomopoulos S, Nir T, Jahanshad N, DeCarli C, Knaack A, Fletcher E, Harvey D, Tosun-Turgut D, Chen S, Choe M, 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Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease. Journal Of The Royal Statistical Society Series B Statistical Methodology 2024, 86: 1013-1044. PMID: 39279915, PMCID: PMC11398888, DOI: 10.1093/jrsssb/qkae023.Peer-Reviewed Original ResearchFunctional linear regression modelClassification of functional dataOut-of-sample prediction errorsFunctional predictorsFunctional dataCovariance structureDifferential regularizationCortical surface geometryClassification problemDiscriminant directionsManifold domainsParkinson's Progression Markers InitiativeNonlinear domainProgression Markers InitiativePrediction errorLinear regression modelsAlzheimer's Disease Neuroimaging InitiativeTheoretical analysisDiscriminant analysisSimulation settingsAlzheimer's diseaseClassificationEstimationFeatures of Alzheimer's diseaseNeuroscience literature
2021
Testing gene–environment interactions in the presence of confounders and mismeasured environmental exposures
Cheng C, Spiegelman D, Wang Z, Wang M. Testing gene–environment interactions in the presence of confounders and mismeasured environmental exposures. G3: Genes, Genomes, Genetics 2021, 11: jkab236. PMID: 34568916, PMCID: PMC8473983, DOI: 10.1093/g3journal/jkab236.Peer-Reviewed Original ResearchConceptsStandard logistic regression approachGreater statistical powerStatistical powerBinary disease outcomeComputational efficiencyIllustrative exampleComputation timeExtensive simulation experimentsMost simulation scenariosMeasurement errorRegression approachConsideration adjustmentsSimulation experimentsExposure measurement errorReverse testLogistic regression approachSimulation scenariosLinear discriminant analysisApproachReverse approachPowerErrorDiscriminant analysis
2013
Differential-Private Data Publishing Through Component Analysis.
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2003
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data
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1994
Selection of best bases for classification and regression
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