[教程] SAS_STAT9.2 User’s Guide, Second Edition The MCMC Procedure

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Chapter 52 TheMCMCProcedure Contents Overview:MCMCProcedure ............................ 3478 PROCMCMCComparedwithOtherSASProcedures ............3479 . j  o% l  A8 HGettingStarted:MCMCProcedure .......................... 3479 ' D6 C$ s) C* H. ~; [: l8 aSimpleLinearRegression ...........................3480 " g* F! V; g, K: |) d  `' y" qTheBehrens-FisherProblem ..........................3488 4 l+ P' s6 z$ hMixed-EffectsModel .............................3492 Syntax:MCMCProcedure .............................. 3495 " W4 e! L% ~/ \PROCMCMCStatement ...........................3496 ARRAYStatement ...............................3508 % o. E. E7 s3 z6 e$ {- pBEGINCNST/ENDCNSTStatement .....................3509 + [# L; B( s8 [4 j6 ?BEGINNODATA/ENDNODATAStatements .................3511 BYStatement .................................3511 MODELStatement ...............................3512 7 u6 A6 \. c! t: l9 ~PARMSStatement ...............................3515 ( Q6 A! U! Y- M' z7 v6 ]. {PRIOR/HYPERPRIORStatement .......................3516 " C# K) i& B7 V: K4 TProgrammingStatements ...........................3516 4 n$ J, x5 k: Q1 lUDSStatement .................................3518 Details:MCMCProcedure .............................. 3522 " U6 \3 H& d' C0 m9 i0 YHowPROCMCMCWorks ..........................3522 BlockingofParameters ............................3523 Samplers ....................................3524 / N& h5 Y. E" x" c4 U3 f% ^4 aTuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 & _  h# o6 \* I( K1 q; CAssignmentsofParameters ..........................3528 ! o' Y: A. \% bStandardDistributions .............................3530 3 `2 ]( O5 {# S  j5 \SpecifyingaNewDistribution .........................3541 + O9 {# |% o- u' K6 ]UsingDensityFunctionsintheProgrammingStatements ...........3542 TruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 ( F) J9 N( W" [( SSomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 1 l1 V7 z$ {, yRegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 5 _8 }1 K. ~. s8 vHandlingofMissingData ...........................3565 $ D3 q3 ^. A! k" c( aFloatingPointErrorsandOverflows ......................3565 HandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 DisplayedOutput ................................3571 . h" S, |+ D7 X  j, SODSTableNames ...............................3575 0 U% s8 ]; ~; YODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 ; Q: ~% n" f1 O6 {. {7 \" c' R% VExample52.2:Box-CoxTransformation ...................3583 Example52.3:GeneralizedLinearModels ..................3592 # t# A1 V) T$ M+ S! h0 _% N! P. h# ^Example52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 ' L$ {# b4 d8 a6 j6 J  b, y9 eExample52.6:ChangePointModels .....................3630 Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 Example52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 Example52.10:ConstrainedAnalysis ....................3666 Example52.11:ImplementaNewSamplingAlgorithm ...........3672 Example52.12:UsingaTransformationtoImproveMixing .........3683 " P# ~3 D( i) ]Example52.13:Gelman-RubinDiagnostics .................3693 . q8 S' w' c* M! P5 G# OReferences ...................................... 3700 ( G: H2 Q8 x, l0 S: ^. t) W& Z 游客，如果您要查看本帖隐藏内容请回复

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