The MCMC Procedure
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Overview
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Getting Started
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Syntax
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DetailsHow PROC MCMC WorksBlocking of ParametersSampling MethodsTuning the Proposal DistributionDirect SamplingConjugate SamplingInitial Values of the Markov ChainsAssignments of ParametersStandard DistributionsUsage of Multivariate DistributionsSpecifying a New DistributionUsing Density Functions in the Programming StatementsTruncation and CensoringSome Useful SAS FunctionsMatrix Functions in PROC MCMCCreate Design MatrixModeling Joint LikelihoodRegenerating Diagnostics PlotsCaterpillar PlotAutocall Macros for PostprocessingGamma and Inverse-Gamma DistributionsPosterior Predictive DistributionHandling of Missing DataFunctions of Random-Effects ParametersFloating Point Errors and OverflowsHandling Error MessagesComputational ResourcesDisplayed OutputODS Table NamesODS Graphics
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ExamplesSimulating Samples From a Known DensityBox-Cox TransformationLogistic Regression Model with a Diffuse PriorLogistic Regression Model with Jeffreys’ PriorPoisson RegressionNonlinear Poisson Regression ModelsLogistic Regression Random-Effects ModelNonlinear Poisson Regression Multilevel Random-Effects ModelMultivariate Normal Random-Effects ModelMissing at Random AnalysisNonignorably Missing Data (MNAR) AnalysisChange Point ModelsExponential and Weibull Survival AnalysisTime Independent Cox ModelTime Dependent Cox ModelPiecewise Exponential Frailty ModelNormal Regression with Interval CensoringConstrained AnalysisImplement a New Sampling AlgorithmUsing a Transformation to Improve MixingGelman-Rubin Diagnostics
- References
Subsections:
- 59.1 Simulating Samples From a Known Density
- 59.2 Box-Cox Transformation
- 59.3 Logistic Regression Model with a Diffuse Prior
- 59.4 Logistic Regression Model with Jeffreys’ Prior
- 59.5 Poisson Regression
- 59.6 Nonlinear Poisson Regression Models
- 59.7 Logistic Regression Random-Effects Model
- 59.8 Nonlinear Poisson Regression Multilevel Random-Effects Model
- 59.9 Multivariate Normal Random-Effects Model
- 59.10 Missing at Random Analysis
- 59.11 Nonignorably Missing Data (MNAR) Analysis
- 59.12 Change Point Models
- 59.13 Exponential and Weibull Survival Analysis
- 59.14 Time Independent Cox Model
- 59.15 Time Dependent Cox Model
- 59.16 Piecewise Exponential Frailty Model
- 59.17 Normal Regression with Interval Censoring
- 59.18 Constrained Analysis
- 59.19 Implement a New Sampling Algorithm
- 59.20 Using a Transformation to Improve Mixing
- 59.21 Gelman-Rubin Diagnostics