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Hybridizing Statistical Computation with SAS and MATLAB 1.0

  Date Added: May 09, 2013  |  Visits: 288

Hybridizing Statistical Computation with SAS and MATLAB

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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 functionGASub_SAS_ModelCovComp_20090805_183427.out: matlab output generated by code

Requirements: No special requirements
Platforms: Matlab
Keyword: Ability Advantageous Advantages Algorithm Allows Us Anal And Sometimes Application At An Available To Basically Best Ga Bestsas Sas Disadvantages Fit International Results Sas Type
Users rating: 0/10

License: Freeware Size: 1.26 MB
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