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

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Chapter 52 3 @0 ^& d/ X7 Q( qTheMCMCProcedure Contents 5 J5 ~& ?( k  F. wOverview:MCMCProcedure ............................ 3478 , @  u4 R, j+ f( `2 a) L8 L1 WPROCMCMCComparedwithOtherSASProcedures ............3479 GettingStarted:MCMCProcedure .......................... 3479 0 r6 W9 V: b9 D, p' h; nSimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 ' H4 n6 c2 K6 t& f1 b$ cMixed-EffectsModel .............................3492 Syntax:MCMCProcedure .............................. 3495 PROCMCMCStatement ...........................3496 6 v: O3 d' I! c$ V* B; V$ _ARRAYStatement ...............................3508 " r0 Q, N; n, BBEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 BYStatement .................................3511 MODELStatement ...............................3512 PARMSStatement ...............................3515 " \- Y2 y+ t; g( W5 [1 FPRIOR/HYPERPRIORStatement .......................3516 ' V/ ], Z# L' l* L# y3 ^$ I6 @4 IProgrammingStatements ...........................3516 UDSStatement .................................3518 Details:MCMCProcedure .............................. 3522 3 |$ J/ |% B( A4 C+ MHowPROCMCMCWorks ..........................3522 1 Y" P0 ?0 @( X0 EBlockingofParameters ............................3523 Samplers ....................................3524 $ U, D' Y& ?9 f9 O4 ]TuningtheProposalDistribution .......................3525 7 v: W  _8 f6 lInitialValuesoftheMarkovChains ......................3528 - y: C* ?3 `& R+ H" T% dAssignmentsofParameters ..........................3528 StandardDistributions .............................3530 2 `3 w% |* Q+ A3 T4 _SpecifyingaNewDistribution .........................3541 : i) X# I: i3 [UsingDensityFunctionsintheProgrammingStatements ...........3542 & J, W( y/ u6 m; }9 H5 vTruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 8 I3 W3 k4 c( dSomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ( _" c3 f3 f7 q/ d8 A: }; Z# n, M% JModelingJointLikelihood ...........................3556 ' C9 m1 q8 ~- Z( z5 V  a: ~7 @! S3 FRegeneratingDiagnosticsPlots ........................3557 : h' z8 l- s  G6 T2 x6 M5 nPosteriorPredictiveDistribution ........................3560 1 D. P/ p0 m6 IHandlingofMissingData ...........................3565 ) K% x2 R* n" l" y2 z' wFloatingPointErrorsandOverflows ......................3565 HandlingErrorMessages ...........................3568 9 C- b( `7 K" @* I: \. b+ g0 t" u- YComputationalResources ...........................3570 DisplayedOutput ................................3571 ODSTableNames ...............................3575 ODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 6 L" M# S$ l4 FExample52.2:Box-CoxTransformation ...................3583 Example52.3:GeneralizedLinearModels ..................3592 Example52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 Example52.6:ChangePointModels .....................3630 # e# a" X  s1 o: Q* V: Z5 }1 eExample52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 ' E  [( s, p# M8 jExample52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 Example52.10:ConstrainedAnalysis ....................3666 7 q  O# [0 |* F* e% x+ WExample52.11:ImplementaNewSamplingAlgorithm ...........3672 & a1 w, d- a. C/ ^" D! LExample52.12:UsingaTransformationtoImproveMixing .........3683 1 v! Q. }, G! c" R2 kExample52.13:Gelman-RubinDiagnostics .................3693 5 p7 u0 J: k% {( XReferences ...................................... 3700 9 y: A0 _6 U4 ~; D9 S 游客，如果您要查看本帖隐藏内容请回复

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