The SAS® System provides a powerful and flexible programming language in a dynamic, interactive environment for programmers, statisticians, and researchers. Use the SAS System for data manipulation and general statistical analysis, then employ SAS/IML software’s interactive matrix language for more specific analysis and exploration. The fundamental data element in SAS/IML is the matrix, a two-dimensional (row-by-column) array of numeric or character values. You do not need to declare, dimension, or allocate storage for a data matrix because SAS/IML software does this automatically. You can change the dimension or type of a matrix, reset options, or replace modules at any time. You can open multiple files or access many libraries. Built-in matrix operators, functions, and subroutines can be applied to perform complex tasks such as matrix inversion or eigenvector generation.

References in zbMATH (referenced in 23 articles )

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  1. Lyles, Robert H.; Kupper, Lawrence L.: Approximate and pseudo-likelihood analysis for logistic regression using external validation data to model log exposure (2013)
  2. Zhao, Zheng; Zhang, Ruiwen; Cox, James; Duling, David; Sarle, Warren: Massively parallel feature selection: an approach based on variance preservation (2013)
  3. Sajobi, Tolulope T.; Lix, Lisa M.; Dansu, Bolanle M.; Laverty, William; Li, Longhai: Robust descriptive discriminant analysis for repeated measures data (2012)
  4. Buonaccorsi, John P.: Measurement error. Models, methods and applications (2010)
  5. Perrett, Jamis J.: A SAS/IML companion for linear models. (2010)
  6. Wright, Stephen E.; Sigal, Belle M.; Bailer, A.John: Workweek optimization of experimental designs: exact designs for variable sampling costs (2010)
  7. Ajmani, Vivek B.: Applied econometrics using the SAS system (2009)
  8. Huque, Mohammad F.; Alosh, Mohamed: A flexible fixed-sequence testing method for hierarchically ordered correlated multiple endpoints in clinical trials (2008)
  9. Pradhan, Vivek; Banerjee, Tathagata: Confidence interval of the difference of two independent binomial proportions using weighted profile likelihood (2008)
  10. Wang, Qinggang; Koval, John J.; Mills, Catherine A.; Lee, Kang-In David: Determination of the selection statistics and best significance level in backward stepwise logistic regression (2008)
  11. Gatu, Cristian; Gentle, James; Hinde, John; Huh, Moon: Special issue on statistical algorithms and software (2007)
  12. Shieh, Gwowen: A unified approach to power calculation and sample size determination for random regression models (2007)
  13. Hammill, Bradley G.; Preisser, John S.: A SAS/IML software program for GEE and regression diagnostics (2006)
  14. Mitchell, Matthew W.; Genton, Marc G.; Gumpertz, Marcia L.: A likelihood ratio test for separability of covariances (2006)
  15. Shieh, Gwowen: Exact interval estimation, power calculation, and sample size determination in normal correlation analysis (2006)
  16. Kistner, Emily O.; Muller, Keith E.: Exact distributions of intraclass correlation and Cronbach’s alpha with Gaussian data and general covariance (2004)
  17. Vu, Hien T.V.: Estimation in semiparametric conditional shared frailty models with events before study entry (2004)
  18. Renard, Didier; Molenberghs, Geert; Geys, Helena: A pairwise likelihood approach to estimation in multilevel probit models (2003)
  19. Lawal, H.Bayo: Modeling the 1984-1993 American League baseball results as dependent categorical data (2002)
  20. Lin, Suh-Yun Elva; Schuff, David; St.Louis, Robert D.: Subscript-free modeling languages: A tool for facilitating the formulation and use of models (2000)

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