Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables
International Journal of Statistical Distributions and Applications
Volume 3, Issue 2, June 2017, Pages: 18-24
Received: May 15, 2017; Accepted: May 24, 2017; Published: Oct. 18, 2017
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Authors
Egbo Ikechukwu, Department of Mathematics, Alvan Ikoku Federal College of Education, Owerri, Nigeria
Uwakwe Joy Ijeoma, Department of Mathematics, Alvan Ikoku Federal College of Education, Owerri, Nigeria
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Abstract
This paper focuses on the Asymptotic Classification Procedures in Two Group Discriminate Analysis with Multivariate Binary Variables. Two data patterns were simulated using the R-Software Statistical Analysis System 2.15.3 and was subjected to two linear classification namely; Location and Logistic Models. To judge the performance of these models, the apparent error rates for each procedure are obtained for different sample sizes. The results obtained show that the location model performed better than Logistic Discrimination with the variation in the error rates being higher for Logistic Discrimination rule.
Keywords
Apparent Error Rates, Location Model, Logistics Classification Rule, Multivariate and Binary Variable
To cite this article
Egbo Ikechukwu, Uwakwe Joy Ijeoma, Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables, International Journal of Statistical Distributions and Applications. Vol. 3, No. 2, 2017, pp. 18-24. doi: 10.11648/j.ijsd.20170302.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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