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Variable Selection in Multivariable Regression Using SAS/IML

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  • Al-Subaihi, Ali A.

Abstract

This paper introduces a SAS/IML program to select among the multivariate model candidates based on a few well-known multivariate model selection criteria. Stepwise regression and all-possible-regression are considered. The program is user friendly and requires the user to paste or read the data at the beginning of the module, include the names of the dependent and independent variables (the y's and the x's), and then run the module. The program produces the multivariate candidate models based on the following criteria: Forward Selection, Forward Stepwise Regression, Backward Elimination, Mean Square Error, Coefficient of Multiple Determination, Adjusted Coefficient of Multiple Determination, Akaike's Information Criterion, the Corrected Form of Akaike's Information Criterion, Hannan and Quinn Information Criterion, the Corrected Form of Hannan and Quinn (HQc) Information Criterion, Schwarz's Criterion, and Mallow's PC. The output also constitutes detailed as well as summarized results.

Suggested Citation

  • Al-Subaihi, Ali A., 2002. "Variable Selection in Multivariable Regression Using SAS/IML," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i12).
    • Handle: RePEc:jss:jstsof:v:007:i12
    DOI: http://hdl.handle.net/10.18637/jss.v007.i12
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    Cited by:

    1. M. Abdellatif & W. Atherton & R. Alkhaddar & Y. Osman, 2015. "Flood risk assessment for urban water system in a changing climate using artificial neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(2), pages 1059-1077, November.

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