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

建不了的模。

Chapter 52 TheMCMCProcedure Contents Overview:MCMCProcedure ............................ 3478 PROCMCMCComparedwithOtherSASProcedures ............3479 ( z% o  k9 g1 _: n$ Y3 F( L# sGettingStarted:MCMCProcedure .......................... 3479 SimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 # |1 G+ n1 l1 V, P7 e0 }Mixed-EffectsModel .............................3492 9 \( t# d8 e2 DSyntax:MCMCProcedure .............................. 3495 PROCMCMCStatement ...........................3496 - O. s' h7 H* X: H, R' VARRAYStatement ...............................3508 BEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 , a* |, R% p; S4 W  |BYStatement .................................3511 8 b! g' Y8 A; \MODELStatement ...............................3512 7 `  X) a% p8 C$ D9 L. lPARMSStatement ...............................3515 / d; W% m; V5 X6 l6 I: g$ m9 |PRIOR/HYPERPRIORStatement .......................3516 / ~" i- j1 R) I9 `3 eProgrammingStatements ...........................3516 UDSStatement .................................3518 1 k+ `8 e; U, V0 _$ BDetails:MCMCProcedure .............................. 3522 0 p5 t  o  b# W. W- Z( f- R& Y1 JHowPROCMCMCWorks ..........................3522   x5 U7 k( e3 m2 W' |. SBlockingofParameters ............................3523 Samplers ....................................3524 & S7 C& h) N  u1 ZTuningtheProposalDistribution .......................3525 . f; j" [# i, U; l' BInitialValuesoftheMarkovChains ......................3528 ) n# m0 H9 a4 b1 l5 `5 \1 _8 h0 ~AssignmentsofParameters ..........................3528 # M3 |- V7 Z  wStandardDistributions .............................3530 3 H) c/ G! W! g7 H% o2 t2 F) }  s0 jSpecifyingaNewDistribution .........................3541 UsingDensityFunctionsintheProgrammingStatements ...........3542 TruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 $ L6 E, y" l5 k# S: p: t! JSomeUsefulSASFunctions ..........................3549 ; y% w; S" g! y) S# JMatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 6 y8 p3 X6 v8 l+ ~RegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 HandlingofMissingData ...........................3565 ) W# L9 H8 f9 k: N6 eFloatingPointErrorsandOverflows ......................3565 HandlingErrorMessages ...........................3568 ; z6 \* N  ^4 N! Y6 S$ y1 QComputationalResources ...........................3570 DisplayedOutput ................................3571 ODSTableNames ...............................3575 8 s7 l0 `8 S0 X9 U0 U, n) I, yODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 $ B6 f7 n/ a- QExample52.2:Box-CoxTransformation ...................3583 Example52.3:GeneralizedLinearModels ..................3592 Example52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 5 S/ T3 m. m7 J9 oExample52.6:ChangePointModels .....................3630 Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 Example52.8:CoxModels ..........................3647   f6 U9 |$ W' w: z7 K/ {' aExample52.9:NormalRegressionwithIntervalCensoring .........3664 / P/ P6 q* `: Q+ aExample52.10:ConstrainedAnalysis ....................3666 # P- r  H; Z/ o6 {! g5 `1 {Example52.11:ImplementaNewSamplingAlgorithm ...........3672 / w2 v2 U# ]) WExample52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13:Gelman-RubinDiagnostics .................3693 References ...................................... 3700 游客，如果您要查看本帖隐藏内容请回复

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