Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables
Egbo Ikechukwu,
Uwakwe Joy Ijeoma
Issue:
Volume 3, Issue 2, June 2017
Pages:
18-24
Received:
15 May 2017
Accepted:
24 May 2017
Published:
18 October 2017
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.
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, ...
Show More
