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

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Chapter 52 ) N6 f: T: ]7 h8 y+ MTheMCMCProcedure Contents 7 T! p9 ~* I$ z$ _2 A& NOverview:MCMCProcedure ............................ 3478 0 r: \8 \" t# D: L4 I# ~. V/ nPROCMCMCComparedwithOtherSASProcedures ............3479 GettingStarted:MCMCProcedure .......................... 3479 SimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 Mixed-EffectsModel .............................3492 ( l( H% s) A/ O" @5 F6 h$ sSyntax:MCMCProcedure .............................. 3495 3 Y" d7 c9 P8 D& G, L1 x  U+ @/ LPROCMCMCStatement ...........................3496 8 a2 s1 }3 Z% e0 p* y! Y' M% ^' o8 QARRAYStatement ...............................3508 6 C, b0 @# z4 i. q+ Z/ c* gBEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 : _' P$ w' z9 ]3 r, UBYStatement .................................3511 MODELStatement ...............................3512 ( b" K5 o7 ^$ ~, ~: J8 iPARMSStatement ...............................3515 0 H1 Q, b  n/ J1 y# B0 [, X: {/ L( WPRIOR/HYPERPRIORStatement .......................3516 3 Q( q+ X) A1 [: o6 o  M$ C4 xProgrammingStatements ...........................3516 ' j$ d* b  r- W( n8 g1 KUDSStatement .................................3518 Details:MCMCProcedure .............................. 3522 1 |" `( P4 A7 ?+ IHowPROCMCMCWorks ..........................3522 BlockingofParameters ............................3523 + }! V6 C8 u4 U! z' ]& u4 nSamplers ....................................3524 , d7 W$ D0 h% oTuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 AssignmentsofParameters ..........................3528 StandardDistributions .............................3530 8 `2 ~1 \5 ^- o+ s' uSpecifyingaNewDistribution .........................3541 UsingDensityFunctionsintheProgrammingStatements ...........3542 + t* m" T* Z* l' p( c/ u; T3 f1 _TruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 SomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 RegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 0 U* \- a/ r+ e" N* ^HandlingofMissingData ...........................3565 FloatingPointErrorsandOverflows ......................3565 HandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 * |' h+ K, C" E" z6 B' }- ?# U5 V- @  HDisplayedOutput ................................3571 ODSTableNames ...............................3575 ODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 % H3 G) }) j; V9 n' f7 ~- B3 q5 NExample52.2:Box-CoxTransformation ...................3583 1 @6 ~& |4 c) [  R* g% rExample52.3:GeneralizedLinearModels ..................3592 Example52.4:NonlinearPoissonRegressionModels ............3605 ) j; K# s6 D3 F" \' q7 P  Z  uExample52.5:Random-EffectsModels ...................3614 Example52.6:ChangePointModels .....................3630 ) C& w, m$ @0 B3 j; {Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 Example52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 Example52.10:ConstrainedAnalysis ....................3666 + Q. p& S' X" ^( t5 nExample52.11:ImplementaNewSamplingAlgorithm ...........3672 Example52.12:UsingaTransformationtoImproveMixing .........3683 ! c* d" a4 W! N# J) t* sExample52.13:Gelman-RubinDiagnostics .................3693 ) B# E0 ~1 U, x/ JReferences ...................................... 3700 $ `2 h( @1 L2 y. T0 Q- ? 游客，如果您要查看本帖隐藏内容请回复

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