Hybridizing Statistical Computation with SAS and MATLAB

Article & associated files detailing a method to jointly use MATLAB and SAS for statistical analysis

There is a wide variety of computation resources available to statisticians, ranging from the simple Microsoft Excel, to the complex Matlab. Each program has its own set of advantages and disadvantages, and sometimes the ability to use more than one application for modeling can be advantageous. Several researchers have developed methods for this. In this paper we propose a very general method for integrating Matlab and SAS, which allows us to perform basically any type of statistical modeling so desired. We demonstrate this interface with a very interesting example. We have a large cross-sectional time series database for which we want to identify a parsimonious model to predict retail sales in the next time-step. We use a stochastic search algorithm in Matlab, and fit a series of mixed models in SAS. The statistical methods and results won "Best Operative Solution" at an international conference. The zip file includes:

- SASMATLAB_MW.pdf: article
- cladag_vars_notempo7.csv: data
- cladag_anal_SUB_BEST.sas: SAS script created by Matlab that resulted in best GA solution
- GASubsetSelect_CLADAGq2.m: Matlab code implementing GA
- SAS_ModelCovComp.m: Matlab code implementing GA fitness function
GASub_SAS_ModelCovComp_20090805_183427.out: matlab output generated by code