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SUBSET: Best Subsets using Information Criteria

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  • Dayton, C. Mitchell

Abstract

SUBSET, written in the matrix language Gauss, is a program that identifies optimal subsets of means or proportions based on independent groups. All possible configurations of ordered subsets of groups are identified and the best model is selected using either the AIC or BIC information criterion. For means, both homogeneous and heterogeneous variance cases are considered. SUBSET offers an alternative approach to traditional post-hoc multiple-comparison procedures such as the Tukey test for pairwise comparisons. Major advantages of SUBSET over traditional pairwise comparison procedures include the fact that intransitive decisions are avoided and that issues related to type I error control, sample size and heterogeneity of variance do not arise.

Suggested Citation

  • Dayton, C. Mitchell, 2001. "SUBSET: Best Subsets using Information Criteria," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 6(i02).
    • Handle: RePEc:jss:jstsof:v:006:i02
    DOI: http://hdl.handle.net/10.18637/jss.v006.i02
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    References listed on IDEAS

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    1. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    2. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
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