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The MCMC Procedure

Overview
PROC MCMC Compared with Other SAS Procedures
Getting Started
Simple Linear Regression The Behrens-Fisher Problem Random-Effects Model
Syntax
PROC MCMC Statement ARRAY Statement BEGINCNST/ENDCNST Statement BEGINNODATA/ENDNODATA Statements BY Statement MODEL Statement PARMS Statement PREDDIST Statement PRIOR/HYPERPRIOR Statement Programming Statements RANDOM Statement UDS Statement
Details
How PROC MCMC Works Blocking of Parameters Sampling Methods Tuning the Proposal Distribution Direct Sampling Conjugate Sampling Initial Values of the Markov Chains Assignments of Parameters Standard Distributions Usage of Multivariate Distributions Specifying a New Distribution Using Density Functions in the Programming Statements Truncation and Censoring Some Useful SAS Functions Matrix Functions in PROC MCMC Create Design Matrix Modeling Joint Likelihood Access Lag and Lead Variables CALL ODE and CALL QUAD Subroutines Regenerating Diagnostics Plots Caterpillar Plot Autocall Macros for Postprocessing Gamma and Inverse-Gamma Distributions Posterior Predictive Distribution Handling of Missing Data Functions of Random-Effects Parameters Floating Point Errors and Overflows Handling Error Messages Computational Resources Displayed Output ODS Table Names ODS Graphics
Examples
Simulating Samples From a Known Density Box-Cox Transformation Logistic Regression Model with a Diffuse Prior Logistic Regression Model with Jeffreys’ Prior Poisson Regression Nonlinear Poisson Regression Models Logistic Regression Random-Effects Model Nonlinear Poisson Regression Multilevel Random-Effects Model Multivariate Normal Random-Effects Model Missing at Random Analysis Nonignorably Missing Data (MNAR) Analysis Change Point Models Exponential and Weibull Survival Analysis Time Independent Cox Model Time Dependent Cox Model Piecewise Exponential Frailty Model Normal Regression with Interval Censoring Constrained Analysis Implement a New Sampling Algorithm Using a Transformation to Improve Mixing Gelman-Rubin Diagnostics One-Compartment Model with Pharmacokinetic Data
References

Examples: MCMC Procedure

Subsections:

73.1 Simulating Samples From a Known Density
73.2 Box-Cox Transformation
73.3 Logistic Regression Model with a Diffuse Prior
73.4 Logistic Regression Model with Jeffreys’ Prior
73.5 Poisson Regression
73.6 Nonlinear Poisson Regression Models
73.7 Logistic Regression Random-Effects Model
73.8 Nonlinear Poisson Regression Multilevel Random-Effects Model
73.9 Multivariate Normal Random-Effects Model
73.10 Missing at Random Analysis
73.11 Nonignorably Missing Data (MNAR) Analysis
73.12 Change Point Models
73.13 Exponential and Weibull Survival Analysis
73.14 Time Independent Cox Model
73.15 Time Dependent Cox Model
73.16 Piecewise Exponential Frailty Model
73.17 Normal Regression with Interval Censoring
73.18 Constrained Analysis
73.19 Implement a New Sampling Algorithm
73.20 Using a Transformation to Improve Mixing
73.21 Gelman-Rubin Diagnostics
73.22 One-Compartment Model with Pharmacokinetic Data

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