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时间:2019-05-12
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1、MCMCEstimationforRandomEffectModelling–TheMLwiNexperienceDrWilliamJ.BrowneSchoolofMathematicalSciencesUniversityofNottinghamContentsRandomeffectmodelling,MCMCandMLwiN.Methodscomparison–Guatemalanchildhealthexample.ExtendibilityofMCMCalgorithms:Crossclas
2、sifiedandmultiplemembershipmodels.ArtificialinseminationandDanishchickenexamples.FurtherExtensions.RandomeffectmodelsModelsthataccountfortheunderlyingstructureinthedataset.Originallydevelopedfornestedstructures(multilevelmodels),forexampleineducation,pu
3、pilsnestedwithinschools.Anextensionoflinearmodellingwiththeinclusionofrandomeffects.Atypical2-levelmodelisHereiindexespupilsandjindexesschools.MLwiNSoftwarepackagedesignedspecificallyforfittingmultilevelmodels.DevelopedbyateamledbyHarveyGoldsteinandJonR
4、asbashattheInstituteofEducationinLondonoverpast15yearsorso.EarlierincarnationsML2,ML3,MLN.Originallycontained‘classical’IGLSestimationmethodsforfittingmodels.MLwiNlaunchedin1998alsoincludedMCMCestimation.MyroleinteamwasasdeveloperofMCMCfunctionalityinML
5、wiNduring4.5yearsattheIOE.EstimationMethodsforMultilevelModelsDuetoadditionalrandomeffectsnosimplematrixformulaeexistforfindingestimatesinmultilevelmodels.Twoalternativeapproachesexist:Iterativealgorithmse.g.IGLS,RIGLS,EMinHLMthatalternatebetweenestimat
6、ingfixedandrandomeffectsuntilconvergence.CanproduceMLandREMLestimates.Simulation-basedBayesianmethodse.g.MCMCthatattempttodrawsamplesfromtheposteriordistributionofthemodel.MCMCAlgorithmConsiderthe2-levelmodelMCMCalgorithmsworkinaBayesianframeworkandsowe
7、needtoaddpriordistributionsfortheunknownparameters.Herethereare4setsofunknownparameters:WewilladdpriordistributionsMCMCAlgorithm(2)Thealgorithmforthismodeltheninvolvessimulatinginturnfromthe4setsofconditionaldistributions.SuchanalgorithmisknownasGibbsSa
8、mpling.MLwiNusesGibbssamplingforallnormalresponsemodels.Firstlywesetstartingvaluesforeachgroupofunknownparameters,Thensamplefromthefollowingconditionaldistributions,firstlyToget.MCMCAlgorithm(3)Wenextsamplefromtoget,thentoget,the
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