[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 5 z' g5 J0 F( w$ @PROCMCMCComparedwithOtherSASProcedures ............3479 2 \. ~8 _/ l6 z! I0 o, e' ~GettingStarted:MCMCProcedure .......................... 3479 4 z: L2 o* W  B: S% |: FSimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 Mixed-EffectsModel .............................3492 Syntax:MCMCProcedure .............................. 3495 1 |1 a- q2 r& d5 CPROCMCMCStatement ...........................3496 # P9 P0 y2 C( ]5 H9 O( I$ w+ Y* AARRAYStatement ...............................3508 $ P3 [9 Z7 r& ~: {2 XBEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 % W1 l8 V! w- a( o( u9 `  f) p0 mBYStatement .................................3511 MODELStatement ...............................3512 PARMSStatement ...............................3515 PRIOR/HYPERPRIORStatement .......................3516 3 I2 u6 x! A' `/ f. ^& nProgrammingStatements ...........................3516 UDSStatement .................................3518 Details:MCMCProcedure .............................. 3522 HowPROCMCMCWorks ..........................3522 BlockingofParameters ............................3523 Samplers ....................................3524 * x6 h: N* y( i* h' vTuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 ! s; c, V% T0 R( J! O9 a! [/ N+ UAssignmentsofParameters ..........................3528 , r9 _! d& B+ {0 X$ U! x/ A9 J4 @, ?StandardDistributions .............................3530 8 A% g+ }8 u5 q5 u/ tSpecifyingaNewDistribution .........................3541 UsingDensityFunctionsintheProgrammingStatements ...........3542 % X" d$ W& R9 I& `4 k/ b( @' @TruncationandCensoring ...........................3544 % @+ Z4 Z; l0 [6 J% g8 UMultivariateDensityFunctions ........................3546 SomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ( C6 ]8 C  U) y6 b4 `0 m( I" QModelingJointLikelihood ...........................3556 # j: `2 T: H+ f0 L, t9 QRegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 6 s. g+ p, H4 x1 vHandlingofMissingData ...........................3565 4 C3 P  m) {" j7 kFloatingPointErrorsandOverflows ......................3565 HandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 DisplayedOutput ................................3571 ODSTableNames ...............................3575 - }) a" C! G1 s7 A: O: yODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 Example52.2:Box-CoxTransformation ...................3583 / t, i! B0 U& `( Y& Q- x( z5 yExample52.3:GeneralizedLinearModels ..................3592 , J: F6 t* ~& dExample52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 Example52.6:ChangePointModels .....................3630 Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 ! y- O$ @' f$ w" {, DExample52.8:CoxModels ..........................3647 1 }9 \2 v4 X$ b. K, AExample52.9:NormalRegressionwithIntervalCensoring .........3664 6 m/ `: N6 i& W0 jExample52.10:ConstrainedAnalysis ....................3666 8 k6 W# Q# i: F% g( A; m1 YExample52.11:ImplementaNewSamplingAlgorithm ...........3672 Example52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13:Gelman-RubinDiagnostics .................3693 / q1 T8 o$ }/ \2 p+ n5 ~# yReferences ...................................... 3700 8 B0 R1 m4 L9 H3 [0 ?* I  w 游客，如果您要查看本帖隐藏内容请回复

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