# SAS/STAT^{®}

### 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

### Spatial Analysis

### Survey Sampling and Analysis

### Videos

SAS/STAT Video Portal.### 2017 Papers

**Advanced Hierarchical Modeling with the MCMC Procedure**

Chen, Fang; Stokes, Maura; SAS Institute, Inc. 2017This 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. 2017This 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. 2017This 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. 2017This 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. 2017This 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. 2017This 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.