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 169 articles )

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  1. Dey, Sanku; Mazucheli, Josmar; Anis, M.Z.: Estimation of reliability of multicomponent stress-strength for a Kumaraswamy distribution (2017)
  2. Filipiak, Katarzyna; Klein, Daniel; Roy, Anuradha: A comparison of likelihood ratio tests and Rao’s score test for three separable covariance matrix structures (2017)
  3. Ford, Whitney P.; Westgate, Philip M.: Improved standard error estimator for maintaining the validity of inference in cluster randomized trials with a small number of clusters (2017)
  4. Lewis, Taylor H.: Complex survey data analysis with SAS (2017)
  5. Staggs, Vincent S.: Comparison of naïve, Kenward-Roger, and parametric bootstrap interval approaches to small-sample inference in linear mixed models (2017)
  6. Yuan, Ke-Hai; Bentler, Peter M.: Improving the convergence rate and speed of Fisher-scoring algorithm: ridge and anti-ridge methods in structural equation modeling (2017)
  7. Bailey, R.A.; Brien, C.J.: Randomization-based models for multitiered experiments. I: A chain of randomizations (2016)
  8. Gosho, Masahiko: Model selection in the weighted generalized estimating equations for longitudinal data with dropout (2016)
  9. Lyles, Robert H.; Mitchell, Emily M.; Weinberg, Clarice R.; Umbach, David M.; Schisterman, Enrique F.: An efficient design strategy for logistic regression using outcome-and covariate-dependent pooling of biospecimens prior to assay (2016)
  10. Ma, Yan; Chu, Haitao; Mazumdar, Madhu: Meta-analysis of proportions of rare events -- a comparison of exact likelihood methods with robust variance estimation (2016)
  11. Patterson, David: A three-population constrained discrimination procedure (2016)
  12. Seals, Samantha R.; Katholi, Charles R.; Zhang, Jing; Aban, Inmaculada B.: Evaluating the use of spatial covariance structures in the analysis of cardiovascular imaging data (2016)
  13. Skipka, Guido; Wieseler, Beate; Kaiser, Thomas; Thomas, Stefanie; Bender, Ralf; Windeler, Jürgen; Lange, Stefan: Methodological approach to determine minor, considerable, and major treatment effects in the early benefit assessment of new drugs (2016)
  14. Yamamura, Kohji: Estimation of the predictive ability of ecological models (2016)
  15. Bandyopadhyay, Soutir; Lahiri, Soumendra N.; Nordman, Daniel J.: A frequency domain empirical likelihood method for irregularly spaced spatial data (2015)
  16. Böhning, Dankmar; Mylona, Kalliopi; Kimber, Alan: Meta-analysis of clinical trials with rare events (2015)
  17. Demetrashvili, Nino; van den Heuvel, Edwin R.: Confidence intervals for intraclass correlation coefficients in a nonlinear dose-response meta-analysis (2015)
  18. Hao, C.; von Rosen, D.; von Rosen, T.: Explicit influence analysis in two-treatment balanced crossover models (2015)
  19. Leong, Kwan: What are the important attributes of good mathematics teaching? (2015) MathEduc
  20. Molenaar, Dylan: Heteroscedastic latent trait models for dichotomous data (2015)

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