SAS/STAT software, a component of the SAS System, provides comprehensive statistical tools for a wide range of statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, and nonparametric analysis. A few examples include mixed models, generalized linear models, correspondence analysis, and structural equations.

References in zbMATH (referenced in 347 articles )

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  1. Paolella, Marc S.: Linear models and time-series analysis. Regression, ANOVA, ARMA and GARCH (2019)
  2. Powell, Christopher D.; López, Secundino; France, James: Elementary functions modified for seasonal effects to describe growth in freshwater fish (2019)
  3. Preisser, John S.; Inan, Gul; Powers, James M.; Chu, Haitao: A population-averaged approach to diagnostic test meta-analysis (2019)
  4. Xu, Shizhong: An alternative derivation of Harville’s restricted log likelihood function for variance component estimation (2019)
  5. Alberto Garcia-Hernandez; Dimitris Rizopoulos: %JM: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses (2018) not zbMATH
  6. Arabameri, Abazar; Asemani, Davud; Hadjati, Jamshid: A structural methodology for modeling immune-tumor interactions including pro- and anti-tumor factors for clinical applications (2018)
  7. Bergtold, Jason S.; Pokharel, Krishna P.; Featherstone, Allen M.; Mo, Lijia: On the examination of the reliability of statistical software for estimating regression models with discrete dependent variables (2018)
  8. Delia Voronca; Mulugeta Gebregziabher; Valerie Durkalski-Mauldin; Lei Liu; Leonard Egede: MTPmle: A SAS Macro and Stata Programs for Marginalized Inference in Semi-Continuous Data (2018) not zbMATH
  9. Heinze, Georg; Wallisch, Christine; Dunkler, Daniela: Variable selection -- a review and recommendations for the practicing statistician (2018)
  10. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018) not zbMATH
  11. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  12. Kaya Bahçecitapar, Melike: Some factors affecting statistical power of approximate tests in the linear mixed model for longitudinal data (2018)
  13. Li, Liang; Wu, Chih-Hsien; Ning, Jing; Huang, Xuelin; Shih, Ya-Chen Tina; Shen, Yu: Semiparametric estimation of longitudinal medical cost trajectory (2018)
  14. Ma, Zhihua; Chen, Guanghui: Bayesian methods for dealing with missing data problems (2018)
  15. Peng, Hua; Jeske, Daniel R.; SenGupta, Ashis; Yao, Weixin: Designing one-sided group sequential clinical trials to detect a mixture alternative (2018)
  16. Sofie Pødenphant, Kasper Kristensen, Per B. Brockhoff: The Multiplicative Mixed Model with the mumm R package as a General and Easy Random Interaction Model Tool (2018) arXiv
  17. Steffen, Kyle R.; Epshteyn, Yekaterina; Zhu, Jingyi; Bowler, Megan J.; Deming, Jody W.; Golden, Kenneth M.: Network modeling of fluid transport through sea ice with entrained exopolymeric substances (2018)
  18. Worku, Hailemichael M.; de Rooij, Mark: A multivariate logistic distance model for the analysis of multiple binary responses (2018)
  19. Wyszynski, Karol; Marra, Giampiero: Sample selection models for count data in R (2018)
  20. Zachariah Neville, Naomi Brownstein: Macros to Conduct Tests of Multimodality in SAS (2018) arXiv

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