SAS/STAT Software Examples

The following SAS/STAT software examples are grouped according to the type of statistical analysis that is being performed. These examples are not included in the SAS/STAT documentation and are available only on the Web.

Bayesian Analysis

• Bayesian Zero-Inflated Poisson Regression
• Bayesian Exponential Mixture Model
• Bayesian Linear Regression with Standardized Covariates
• Bayesian Hierarchical Modeling for Meta-Analysis
• Bayesian Hierarchical Poisson Regression Model for Overdispersed Count Data Using SAS/STAT 9.2
• Bayesian Hierarchical Poisson Regression Model for Overdispersed Count Data Using SAS/STAT 9.3
• Bayesian Binomial Model with Power Prior Using the MCMC Procedure
• Bayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.2
• Bayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.3
• Bayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.2
• Bayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.3
• Bayesian Quantile Regression
• Bayesian LASSO
• Stochastic Search Variable Selection with PROC MCMC
• Bayesian IRT Models: Unidimensional Binary Models
• Bayesian Unidimensional IRT Models: Graded Response Model
• Bayesian Autoregressive and Time-Varying Coefficients Time Series Models

Generalized Linear Models

• Fitting Zero-Inflated Count Data Models by Using PROC GENMOD
• High-Performance Variable Selection for Generalized Linear Models: PROC HPGENSELECT
• Fitting Tweedie's Compound Poisson-Gamma Mixture Model by Using PROC HPGENSELECT

Cluster Analysis

• Assessing the Accuracy of Cluster Allocations Obtained from Finite Mixture Models

Spatial Analysis

• Handling Spatial Data in Spherical Coordinates

Survey Sampling and Analysis

• Estimating the Variance of a Variable in a Finite Population
• Estimating the Standard Deviation of a Variable in a Finite Population
• Fractional Hot-Deck Imputation for Mixed Variables
• Poststratification with PROC SURVEYMEANS
• Poisson Regressions for Complex Surveys

Videos

Tools for Assessing a Propensity Score Model in SAS/STAT By Michael Lamm Air date: October 26, 2020	Building and Using DAGs for Causal Inference By Clay Thompson Air date: August 28, 2020	What's New in SAS/STAT 15.1 By G. Gordon Brown Air date: December 18, 2019	Scoring Predictive Models with SAS/STAT Software By Phil Gibbs Air date: December 18, 2019
Introducing the BGLIMM Procedure for Bayesian Generalized Linear Mixed Models By Amy Shi Air date: November 4, 2019	PROC GENSELECT and PROC LOGSELECT: Old Friends, New Clothes! By Dave Kessler Air date: October 21, 2019	Model-Based Clustering with PROC MBC By Dave Kessler Air date: October 21, 2019	Handling Excess Zeros with FMM Procedure (PROC FMM) By Dave Kessler Air date: October 10, 2019
CAUSALGRAPH Procedure for Graphical Causal Model Analysis By Clay Thompson Air date: September 19, 2019	Performing Restricted Mean Survival Time Analysis Using SAS/STAT By Changbin Guo Air date: September 19, 2019	Partial Proportional Odds Modeling with the LOGISTIC Procedure By Bob Derr Air date: August 2, 2019	What's New in SAS/STAT 14.3 By G. Gordon Brown Air date: November 29, 2018

For all SAS/STAT videos, go to the SAS/STAT Video Portal.

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2017 Papers

• Advanced Hierarchical Modeling with the MCMC Procedure
  Chen, Fang; Stokes, Maura; SAS Institute, Inc. 2017

  This paper shows how you can use PROC MCMC to fit hierarchical models that have varying degrees of complexity, from frequently encountered conditional independent models to more involved cases of modeling intricate interdependence.

• Estimating Causal Effects from Observational Data with the CAUSALTRT Procedure
  Lamm, Michael; Yung, Yiu-Fai; SAS Institute, Inc. 2017

  This paper reviews the statistical methods that are implemented in the CAUSALTRT procedure and includes examples of how you can use this procedure to estimate causal effects from observational data.

• Evaluating Predictive Accuracy of Survival Models with PROC PHREG
  Guo, Changbin; So, Ying; Woosung, Jang; SAS Institute, Inc. 2017

  This paper reviews the existing features in PROC LOGISTIC for C-statistic and ROC curves, presents the new features in PROC PHREG, and illustrates their applications in examples. Key differences between PROC PHREG and PROC LOGISTIC are also examined.

• Five Things You Should Know about Quantile Regression
  Rodriguez, Robert N.; Yao, Yonggang; SAS Institute, Inc. 2017

  This paper explains the concepts and benefits of quantile regression, and it introduces you to the appropriate procedures in SAS/STAT software.

• Propensity Score Methods for Causal Inference with the PSMATCH Procedure
  Yuan, Yang; Yung, Yiu-Fai; Stokes, Maura; SAS Institute, Inc. 2017

  This paper reviews propensity score methods for causal inference and introduces the PSMATCH procedure, which is new in SAS/STAT 14.2.

• Step Up Your Statistical Practice with Today’s SAS/STAT Software
  Rodriguez, Robert N.; Gibbs, Phil; Tobias, Randy; SAS Institute, Inc. 2017

  This paper will increase your awareness of modern tools in SAS/STAT by providing high-level comparisons with well-established tools and explaining the benefits of enhancements and new procedures.

For all SAS/STAT papers, go to Statistics Papers and Presentations