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

建不了的模。

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Chapter 52 ' L( ^  ^" b' i4 R5 LTheMCMCProcedure Contents & b3 ^$ N6 X9 _) d+ ^1 wOverview:MCMCProcedure ............................ 3478 . h, f3 @$ T# mPROCMCMCComparedwithOtherSASProcedures ............3479 ) G- }6 `  l/ Z/ M$ d7 w# @2 K  `GettingStarted:MCMCProcedure .......................... 3479 SimpleLinearRegression ...........................3480 $ c( j# S* y$ _7 c* a8 g& ?) \TheBehrens-FisherProblem ..........................3488 Mixed-EffectsModel .............................3492 Syntax:MCMCProcedure .............................. 3495 ' I. {/ T5 x9 W. i3 `4 vPROCMCMCStatement ...........................3496 ARRAYStatement ...............................3508 BEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 BYStatement .................................3511 MODELStatement ...............................3512   }4 |) V/ W1 VPARMSStatement ...............................3515 PRIOR/HYPERPRIORStatement .......................3516 ProgrammingStatements ...........................3516 ) g# E; J  ~2 {7 f. aUDSStatement .................................3518 8 e3 \& v% B0 Z+ C7 Y: tDetails:MCMCProcedure .............................. 3522 ( |6 D8 W& g4 I5 g6 Z# k/ lHowPROCMCMCWorks ..........................3522 ) T6 J5 p0 [! s+ P' T* oBlockingofParameters ............................3523 ! a" j( x6 X6 eSamplers ....................................3524 TuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 , i0 ?4 _% V- @6 F9 `AssignmentsofParameters ..........................3528 StandardDistributions .............................3530 + T5 C# g# e6 X$ j: a5 lSpecifyingaNewDistribution .........................3541 & H! g& z- i9 LUsingDensityFunctionsintheProgrammingStatements ...........3542 2 V# E* ]* _# u! GTruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 , ?4 c/ f$ Q0 S8 K3 O2 ^SomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 RegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 HandlingofMissingData ...........................3565 7 L: P$ q5 @( N5 {6 w3 |FloatingPointErrorsandOverflows ......................3565 + Q2 g; g- M  o; Q% gHandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 ! m3 C5 w/ i( G% n3 R( oDisplayedOutput ................................3571 ODSTableNames ...............................3575 + }) Q& D- F5 j( \ODSGraphics .................................3577 ) h+ E: W$ U" e& `Examples:MCMCProcedure ............................ 3578 ; t$ K( C9 G- k4 w0 t+ AExample52.1:SimulatingSamplesFromaKnownDensity .........3578 Example52.2:Box-CoxTransformation ...................3583 / U$ E8 \3 ]) CExample52.3:GeneralizedLinearModels ..................3592 Example52.4:NonlinearPoissonRegressionModels ............3605 ! p; k2 T1 |8 XExample52.5:Random-EffectsModels ...................3614 : H2 `+ O9 h5 v* [* D9 GExample52.6:ChangePointModels .....................3630 Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 3 B2 i; }( G9 ^( m2 q. ^1 FExample52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 # U+ S8 P, y1 A/ ]5 DExample52.10:ConstrainedAnalysis ....................3666 4 q! g: x. g4 p+ h3 HExample52.11:ImplementaNewSamplingAlgorithm ...........3672 Example52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13:Gelman-RubinDiagnostics .................3693 References ...................................... 3700 + d$ K. [4 s$ {! l- H+ b* b) G 游客，如果您要查看本帖隐藏内容请回复

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