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 inĮxamples. Guo, Changbin So, Ying Woosung, Jang SAS Institute, Inc. Evaluating Predictive Accuracy of Survival Models with PROC PHREG.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. Lamm, Michael Yung, Yiu-Fai SAS Institute, Inc. Estimating Causal Effects from Observational Data with the CAUSALTRT Procedure.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 Advanced Hierarchical Modeling with the MCMC ProcedureĬhen, Fang Stokes, Maura SAS Institute, Inc.Handling Spatial Data in Spherical CoordinatesĮstimating the Variance of a Variable in a Finite PopulationĮstimating the Standard Deviation of a Variable in a Finite Populationįractional Hot-Deck Imputation for Mixed Variablesįor all SAS/STAT videos, go to the SAS/STAT Video Portal. High-Performance Variable Selection for Generalized Linear Models: PROC HPGENSELECTįitting Tweedie's Compound Poisson-Gamma Mixture Model by Using PROC HPGENSELECTĪssessing the Accuracy of Cluster Allocations Obtained from Finite Mixture Models
Sas statistics series#
Stochastic Search Variable Selection with PROC MCMCīayesian IRT Models: Unidimensional Binary Modelsīayesian Unidimensional IRT Models: Graded Response Modelīayesian Autoregressive and Time-Varying Coefficients Time Series Modelsįitting Zero-Inflated Count Data Models by Using PROC GENMOD These examples are not included in the SAS/STAT documentation and are available only on the Web.īayesian Zero-Inflated Poisson Regressionīayesian Linear Regression with Standardized Covariatesīayesian Hierarchical Modeling for Meta-Analysisīayesian Hierarchical Poisson Regression Model for Overdispersed Count Data Using SAS/STAT 9.2īayesian Hierarchical Poisson Regression Model for Overdispersed Count Data Using SAS/STAT 9.3īayesian Binomial Model with Power Prior Using the MCMC Procedureīayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.2īayesian Multivariate Prior for Multiple Linear Regression Using SAS/STAT 9.3īayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.2īayesian Multinomial Model for Ordinal Data Using SAS/STAT 9.3
Sas statistics software#
The following SAS/STAT software examples are grouped according to the type of statistical analysis that is being performed.
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