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纵向数据的一种稳健同时建模方法（英文）.docx

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纵向数据的一种稳健同时建模方法（英文） Title:VerticalData:ARobustApproachtoModeling Abstract: Intheeraofbigdata,thesheervolumeandcomplexityofdatacreatechallengesfortraditionalmodelingtechniques.Onesuchchallengeliesinhandlingverticallystructureddata,wherevariablesaremeasuredovermultipletimepointsoracrossdifferentlevelsofhierarchy.Thispaperpresentsarobustapproachtomodelingverticaldata,whichensuresaccurateandreliableanalysisinthefaceofoutliers,missingdata,andnon-normaldistributions.Wediscusstheimportanceofverticaldatamodeling,outlinethemajorstepsinvolvedintheprocess,andprovideexamplesandapplicationsfromvariousdomains. 1.Introduction Inmanyfieldssuchasfinance,healthcare,andsocialsciences,dataareoftencollectedandstoredinaverticallystructuredformat.Thismeansthatobservationsarerecordedovertimeoracrossdifferentlevelsofhierarchy,resultinginarichsourceofinformationthatcanbeutilizedforanalysisanddecision-making.However,traditionalmodelingtechniquesmaynotbeabletohandlethecomplexitiesandchallengesassociatedwithverticaldata.Hence,thereisaneedforrobustmethodsthatcanaccommodatetheuniquecharacteristicsofsuchdata. 2.TheChallengesofVerticalData Verticaldataposesseveralchallengesthatmakemodelingandanalysisdifficult.First,missingdataisacommonoccurrenceinlongitudinalorhierarchicaldatasets,whichcanintroducebiasesandreducethereliabilityoftheanalysis.Second,outliersandextremevaluescansignificantlyimpacttheresultsifnotproperlyaddressed.Third,theassumptionofnormality,whichisoftenmadeinclassicalstatisticalmethods,maynotholdforverticaldataduetothepresenceofskewness,kurtosis,orheavytails.Lastly,thecorrelationstructureamongobservationsneedstobeadequatelycapturedtoobtainaccurateestimatesandpredictions. 3.ARobustApproachtoVerticalDataModeling Toaddressthechallengesofverticaldata,arobustapproachthatintegratesvarioustechniquesisneeded.Thefollowingstepsoutlineacomprehensivemethodologyformodelingverticaldata: 3.1.DataPreprocessing Thefirststepinvolvesdatacleaningandpreprocessing,whichincludeshandlingmissingdata,identifyi