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

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Chapter 52 / U" r: \0 N% V# YTheMCMCProcedure Contents Overview:MCMCProcedure ............................ 3478 PROCMCMCComparedwithOtherSASProcedures ............3479 + Y& B  f# r) s! Q4 X. }GettingStarted:MCMCProcedure .......................... 3479 SimpleLinearRegression ...........................3480 2 e# A4 M8 j" i8 H9 D0 G# b# LTheBehrens-FisherProblem ..........................3488 Mixed-EffectsModel .............................3492 Syntax:MCMCProcedure .............................. 3495 PROCMCMCStatement ...........................3496 ARRAYStatement ...............................3508 ! l( ^& q- q; e6 QBEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 , W7 f/ y' h' J+ W. v/ U' S2 _BYStatement .................................3511 MODELStatement ...............................3512 : }/ I. E7 X+ L! ?& H  ^PARMSStatement ...............................3515 PRIOR/HYPERPRIORStatement .......................3516 ProgrammingStatements ...........................3516 UDSStatement .................................3518 # N) L. @& ~5 D$ {+ p. wDetails:MCMCProcedure .............................. 3522 + n( |: o. r' m% R- tHowPROCMCMCWorks ..........................3522 , f5 ?  c8 H2 [BlockingofParameters ............................3523 * b- u2 y/ A" a# m: aSamplers ....................................3524 TuningtheProposalDistribution .......................3525 , G; j# C& a! g8 x' K* ^InitialValuesoftheMarkovChains ......................3528 / _/ q8 z2 c7 xAssignmentsofParameters ..........................3528 # j! W' M/ b, g! w# pStandardDistributions .............................3530 * l4 \. s& _: \# [SpecifyingaNewDistribution .........................3541 2 }& }6 c1 x$ V$ N% W0 t/ L1 ?# RUsingDensityFunctionsintheProgrammingStatements ...........3542 9 Y, N% }2 `! n7 d- t) K' m' jTruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 SomeUsefulSASFunctions ..........................3549 " V% I- j: k; b+ T# `7 UMatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 RegeneratingDiagnosticsPlots ........................3557 3 ]9 B6 l1 @' X; VPosteriorPredictiveDistribution ........................3560 3 }" X* a7 c" P* X- a: BHandlingofMissingData ...........................3565 ; r) m# T- p  ]0 `4 h+ u: p$ b0 m3 KFloatingPointErrorsandOverflows ......................3565 HandlingErrorMessages ...........................3568 + U! X1 ]* D( ^! a7 dComputationalResources ...........................3570 DisplayedOutput ................................3571 ODSTableNames ...............................3575 ODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 Example52.2:Box-CoxTransformation ...................3583 8 d) M2 ^) b- Z# W; m( ~) wExample52.3:GeneralizedLinearModels ..................3592 3 C' G2 ]! H  Z( E4 ]. L& RExample52.4:NonlinearPoissonRegressionModels ............3605 ) |. s1 X' i; lExample52.5:Random-EffectsModels ...................3614 Example52.6:ChangePointModels .....................3630 Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 ( ~# N5 O8 k; s; r" }5 m! C: w" CExample52.8:CoxModels ..........................3647 & r7 }" Q  K: e+ Q( VExample52.9:NormalRegressionwithIntervalCensoring .........3664 8 z. E6 r9 [) h5 I  H* t( ^( ^8 ?. A$ uExample52.10:ConstrainedAnalysis ....................3666 7 M2 Y9 D5 N. ^  ^4 bExample52.11:ImplementaNewSamplingAlgorithm ...........3672 / k5 r" c) h* H8 y7 T5 K7 C- n$ YExample52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13:Gelman-RubinDiagnostics .................3693 ( `' v6 P" b! |. d8 P- `# WReferences ...................................... 3700 4 X* [1 A# L0 z. {) i; Y6 @ 游客，如果您要查看本帖隐藏内容请回复

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