SAS/STAT

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 360 articles )

Showing results 1 to 20 of 360.
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  1. Alnosaier, Waseem; Birkes, David: Inner workings of the Kenward-Roger test (2019)
  2. Daniel Sabanés Bové, Wai Yin Yeung, Giuseppe Palermo, Thomas Jaki: Model-Based Dose Escalation Designs in R with crmPack (2019) not zbMATH
  3. Lohr, Sharon L.: Sampling. Design and analysis (2019)
  4. Paolella, Marc S.: Linear models and time-series analysis. Regression, ANOVA, ARMA and GARCH (2019)
  5. Payne, Scott; Fuller, Edgar; Zhang, Cun-Quan: Edge-cuts of optimal average weights (2019)
  6. Powell, Christopher D.; López, Secundino; France, James: Elementary functions modified for seasonal effects to describe growth in freshwater fish (2019)
  7. Preisser, John S.; Inan, Gul; Powers, James M.; Chu, Haitao: A population-averaged approach to diagnostic test meta-analysis (2019)
  8. Theodor Balan; Hein Putter: frailtyEM: An R Package for Estimating Semiparametric Shared Frailty Models (2019) not zbMATH
  9. Xu, Shizhong: An alternative derivation of Harville’s restricted log likelihood function for variance component estimation (2019)
  10. 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
  11. Arabameri, Abazar; Asemani, Davud; Hadjati, Jamshid: A structural methodology for modeling immune-tumor interactions including pro- and anti-tumor factors for clinical applications (2018)
  12. 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)
  13. Castilla, Elena; Martín, Nirian; Pardo, Leandro: Minimum phi-divergence estimators for multinomial logistic regression with complex sample design (2018)
  14. 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
  15. Fumes-Ghantous, Giovana; Ferrari, Silvia L. P.; Corrente, José Eduardo: Box-Cox (t) random intercept model for estimating usual nutrient intake distributions (2018)
  16. Heinze, Georg; Wallisch, Christine; Dunkler, Daniela: Variable selection -- a review and recommendations for the practicing statistician (2018)
  17. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018) not zbMATH
  18. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  19. Kaya Bahçecitapar, Melike: Some factors affecting statistical power of approximate tests in the linear mixed model for longitudinal data (2018)
  20. Li, Liang; Wu, Chih-Hsien; Ning, Jing; Huang, Xuelin; Shih, Ya-Chen Tina; Shen, Yu: Semiparametric estimation of longitudinal medical cost trajectory (2018)

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