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

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Chapter 52 TheMCMCProcedure Contents Overview:MCMCProcedure ............................ 3478 # o. \, F& k# O4 H% |PROCMCMCComparedwithOtherSASProcedures ............3479 GettingStarted:MCMCProcedure .......................... 3479 + u" p* E* y9 n, P1 D/ `" I4 OSimpleLinearRegression ...........................3480 5 R' r9 ]+ E) J5 pTheBehrens-FisherProblem ..........................3488 + \( v+ w6 j1 z* E% `" m0 @Mixed-EffectsModel .............................3492 Syntax:MCMCProcedure .............................. 3495 % f( O! `2 N8 U0 o: z* ZPROCMCMCStatement ...........................3496 % d: b! I/ Z4 ]! c3 Y( F. hARRAYStatement ...............................3508 BEGINCNST/ENDCNSTStatement .....................3509 & e* m; ]2 B+ aBEGINNODATA/ENDNODATAStatements .................3511 5 }4 Z% v  V7 I' |0 W+ }9 C* _8 k6 s) GBYStatement .................................3511 MODELStatement ...............................3512 PARMSStatement ...............................3515 PRIOR/HYPERPRIORStatement .......................3516 ProgrammingStatements ...........................3516 UDSStatement .................................3518 Details:MCMCProcedure .............................. 3522 7 w$ A1 X& R, A3 d- SHowPROCMCMCWorks ..........................3522 % G2 i6 I- Y( \* e3 LBlockingofParameters ............................3523 Samplers ....................................3524 TuningtheProposalDistribution .......................3525   R" G4 L  b3 h* @9 A6 cInitialValuesoftheMarkovChains ......................3528 4 t6 e  ^3 i/ z# MAssignmentsofParameters ..........................3528 : [$ y+ N4 c! ^. c4 [8 XStandardDistributions .............................3530 5 B) C; x6 w  ASpecifyingaNewDistribution .........................3541 ! h. n, o+ C  F- F& zUsingDensityFunctionsintheProgrammingStatements ...........3542 TruncationandCensoring ...........................3544 ( }8 \) B2 s; V$ k/ r: q; Z1 JMultivariateDensityFunctions ........................3546 & v6 ^$ k4 g. a2 w' N3 p! uSomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 " C! Q3 b4 q7 K0 dRegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 2 X" K( g# g  VHandlingofMissingData ...........................3565 ( m# I; G) Q# j3 zFloatingPointErrorsandOverflows ......................3565 4 |5 @2 U8 M( f/ s2 k5 k1 qHandlingErrorMessages ...........................3568 # v9 f2 \* R/ l& I$ M: e# M" v% R7 SComputationalResources ...........................3570 DisplayedOutput ................................3571 8 s- f: t  a& w( R' {% W: O8 ]ODSTableNames ...............................3575 8 x5 A! I% J- h4 S, g* q0 bODSGraphics .................................3577 7 I' `" ?) ]) T' z& Q) \Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 $ d4 C3 j5 h2 oExample52.2:Box-CoxTransformation ...................3583 Example52.3:GeneralizedLinearModels ..................3592 Example52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 Example52.6:ChangePointModels .....................3630 : q  n5 ?  r/ s# U  W% rExample52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 Example52.8:CoxModels ..........................3647 ( _$ R# ^7 G1 T" }1 ]) G* pExample52.9:NormalRegressionwithIntervalCensoring .........3664 4 g4 F8 F! |1 V% rExample52.10:ConstrainedAnalysis ....................3666 Example52.11:ImplementaNewSamplingAlgorithm ...........3672 Example52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13:Gelman-RubinDiagnostics .................3693 6 `* ?9 T2 ]3 |- n$ pReferences ...................................... 3700 + x6 D" x0 z% p: N6 _  g) @ 游客，如果您要查看本帖隐藏内容请回复

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