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

建不了的模。

Chapter 52 TheMCMCProcedure ( C) L  o0 S" U- X0 K% I) VContents Overview:MCMCProcedure ............................ 3478 PROCMCMCComparedwithOtherSASProcedures ............3479 ) v1 K! ^" w0 N* i7 o" \GettingStarted:MCMCProcedure .......................... 3479 0 [" z8 T4 V: ISimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 Mixed-EffectsModel .............................3492 Syntax:MCMCProcedure .............................. 3495 PROCMCMCStatement ...........................3496 ARRAYStatement ...............................3508 BEGINCNST/ENDCNSTStatement .....................3509 / b& @  c  y* S7 Z0 [( gBEGINNODATA/ENDNODATAStatements .................3511 , _# z+ t1 Y# J8 X; f0 LBYStatement .................................3511   o, B9 O( |* ^! NMODELStatement ...............................3512 , @; f" ^8 v, l" H( }: WPARMSStatement ...............................3515 ' X/ i* d% r$ P' \) ePRIOR/HYPERPRIORStatement .......................3516 ( {5 Y) [$ }( E- x) Q1 |4 w. NProgrammingStatements ...........................3516 6 @& `& M: \# r% n$ Y3 `UDSStatement .................................3518 # f4 Q; f4 v5 w, ~0 ODetails:MCMCProcedure .............................. 3522 HowPROCMCMCWorks ..........................3522 3 h  L, f0 }* g. hBlockingofParameters ............................3523 , i8 [1 e, z0 W  k- Z& R8 G2 z# ~Samplers ....................................3524 TuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 AssignmentsofParameters ..........................3528 6 t. a7 ?; F# t0 c4 RStandardDistributions .............................3530 5 L; ^. l5 X2 r  E: ~SpecifyingaNewDistribution .........................3541 3 ]7 E) D8 G" n3 e) R* P1 C, r8 k* nUsingDensityFunctionsintheProgrammingStatements ...........3542 TruncationandCensoring ...........................3544   o3 q  w9 Z. m; Y5 C! D/ aMultivariateDensityFunctions ........................3546 " p* c# c4 b6 T  VSomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 + Z& O( @. `! n6 K8 q; g& r% l0 ^/ PModelingJointLikelihood ...........................3556 2 Z1 V# Z# _( k6 \8 N0 q' s# nRegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 9 T. o9 J" y, S# wHandlingofMissingData ...........................3565 FloatingPointErrorsandOverflows ......................3565 2 [- ]+ t( t# d+ z* c4 e  K6 lHandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 . b' W* ?5 w9 WDisplayedOutput ................................3571 ODSTableNames ...............................3575 2 e# v! V3 v$ g3 k2 O$ r; R/ S; [ODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 ) |) ^  f% ]- y& KExample52.2:Box-CoxTransformation ...................3583   h6 s6 s% n, T2 T( oExample52.3:GeneralizedLinearModels ..................3592 5 b; A+ w# H5 ^* z7 D0 }Example52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 Example52.6:ChangePointModels .....................3630 Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 5 n# N3 ~% ]; C( K: f+ e5 oExample52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 Example52.10:ConstrainedAnalysis ....................3666 Example52.11:ImplementaNewSamplingAlgorithm ...........3672 Example52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13:Gelman-RubinDiagnostics .................3693 References ...................................... 3700 - {' y9 V4 Y$ g! V6 @. {% x/ } 游客，如果您要查看本帖隐藏内容请回复

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