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

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Chapter 52 TheMCMCProcedure Contents ; v9 \: {" Z% [Overview:MCMCProcedure ............................ 3478 1 z- g2 a: e; F/ c1 {PROCMCMCComparedwithOtherSASProcedures ............3479 GettingStarted:MCMCProcedure .......................... 3479 SimpleLinearRegression ...........................3480 % v; T& _0 c# i8 ETheBehrens-FisherProblem ..........................3488 Mixed-EffectsModel .............................3492 & o$ @5 y8 t+ x$ O9 GSyntax:MCMCProcedure .............................. 3495 ; h/ _/ Y3 d8 HPROCMCMCStatement ...........................3496 / y6 O, H- ]6 T4 S3 |) ?  K$ `9 |ARRAYStatement ...............................3508 BEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 BYStatement .................................3511 / E! u$ K1 t3 ?# |& Y$ ]  oMODELStatement ...............................3512 ! t1 r# F  g3 g$ e% mPARMSStatement ...............................3515 PRIOR/HYPERPRIORStatement .......................3516 5 i: W# w  E* d5 A1 E/ _ProgrammingStatements ...........................3516 UDSStatement .................................3518 3 U) z# E: P3 DDetails:MCMCProcedure .............................. 3522 HowPROCMCMCWorks ..........................3522 + Z! C4 N+ I2 UBlockingofParameters ............................3523 Samplers ....................................3524 & |4 K' [4 q# a$ _6 X( @TuningtheProposalDistribution .......................3525   d# g. B( {% s1 T: F5 l5 \InitialValuesoftheMarkovChains ......................3528 & r% T6 \& Q5 q; w$ [4 I% [AssignmentsofParameters ..........................3528 StandardDistributions .............................3530 SpecifyingaNewDistribution .........................3541 # d2 ^3 D2 {" T. n* J; g3 {  a  WUsingDensityFunctionsintheProgrammingStatements ...........3542 4 D3 ~8 p3 p$ l; j/ [TruncationandCensoring ...........................3544 3 t0 X3 C% ^' d4 ZMultivariateDensityFunctions ........................3546 SomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ( `7 S% Y- t& @: `/ eModelingJointLikelihood ...........................3556 RegeneratingDiagnosticsPlots ........................3557 * b/ M- G8 r6 a! PPosteriorPredictiveDistribution ........................3560 3 {+ Y, A8 T4 b. XHandlingofMissingData ...........................3565 $ j1 j1 G' p0 B" PFloatingPointErrorsandOverflows ......................3565 ) F# n: b* b& Q% d% {" a' g- VHandlingErrorMessages ...........................3568 ' E. W4 N5 O& ?ComputationalResources ...........................3570 DisplayedOutput ................................3571 $ I: [( k; {9 a4 G) ~ODSTableNames ...............................3575 2 c+ q" E& N! \2 q& o- K! tODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 ! F# @' o7 ]- \5 ?" r  p2 C# W, hExample52.1:SimulatingSamplesFromaKnownDensity .........3578   u6 u! x4 R; F" ~, z1 W* ]Example52.2:Box-CoxTransformation ...................3583 Example52.3:GeneralizedLinearModels ..................3592 3 L4 F2 |2 b+ g, xExample52.4:NonlinearPoissonRegressionModels ............3605 / b/ N& A5 u# w5 I: j1 j8 R: qExample52.5:Random-EffectsModels ...................3614 Example52.6:ChangePointModels .....................3630 " w" x5 R- ^3 r0 q3 Y" eExample52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 - K- q& C1 f2 q) ^( W: Q! QExample52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 Example52.10:ConstrainedAnalysis ....................3666 Example52.11:ImplementaNewSamplingAlgorithm ...........3672 0 F; P3 [, |; S* u: lExample52.12:UsingaTransformationtoImproveMixing .........3683 ! f8 K- |) L+ z) N7 e/ yExample52.13:Gelman-RubinDiagnostics .................3693 ( [, C% C8 R9 k8 A0 F* ?References ...................................... 3700 游客，如果您要查看本帖隐藏内容请回复

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