Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables

Egbo Ikechukwu; Uwakwe Joy Ijeoma

doi:doi:10.11648/j.ijsd.20170302.12

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Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables

Egbo Ikechukwu, Uwakwe Joy Ijeoma

Published in International Journal of Statistical Distributions and Applications (Volume 3, Issue 2)

Received: 15 May 2017 Accepted: 24 May 2017 Published: 18 October 2017

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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.

Published in	International Journal of Statistical Distributions and Applications (Volume 3, Issue 2)
DOI	10.11648/j.ijsd.20170302.12
Page(s)	18-24
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Apparent Error Rates, Location Model, Logistics Classification Rule, Multivariate and Binary Variable

References

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APA Style

Egbo Ikechukwu, Uwakwe Joy Ijeoma. (2017). Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables. International Journal of Statistical Distributions and Applications, 3(2), 18-24. https://doi.org/10.11648/j.ijsd.20170302.12

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ACS Style

Egbo Ikechukwu; Uwakwe Joy Ijeoma. Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables. Int. J. Stat. Distrib. Appl. 2017, 3(2), 18-24. doi: 10.11648/j.ijsd.20170302.12

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AMA Style

Egbo Ikechukwu, Uwakwe Joy Ijeoma. Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables. Int J Stat Distrib Appl. 2017;3(2):18-24. doi: 10.11648/j.ijsd.20170302.12

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@article{10.11648/j.ijsd.20170302.12,
  author = {Egbo Ikechukwu and Uwakwe Joy Ijeoma},
  title = {Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {3},
  number = {2},
  pages = {18-24},
  doi = {10.11648/j.ijsd.20170302.12},
  url = {https://doi.org/10.11648/j.ijsd.20170302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20170302.12},
  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.},
 year = {2017}
}

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TY  - JOUR
T1  - Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables
AU  - Egbo Ikechukwu
AU  - Uwakwe Joy Ijeoma
Y1  - 2017/10/18
PY  - 2017
N1  - https://doi.org/10.11648/j.ijsd.20170302.12
DO  - 10.11648/j.ijsd.20170302.12
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 18
EP  - 24
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20170302.12
AB  - 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.
VL  - 3
IS  - 2
ER  -

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Author Information

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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APA Style

Egbo Ikechukwu, Uwakwe Joy Ijeoma. (2017). Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables. International Journal of Statistical Distributions and Applications, 3(2), 18-24. https://doi.org/10.11648/j.ijsd.20170302.12

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ACS Style

Egbo Ikechukwu; Uwakwe Joy Ijeoma. Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables. Int. J. Stat. Distrib. Appl. 2017, 3(2), 18-24. doi: 10.11648/j.ijsd.20170302.12

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AMA Style

Egbo Ikechukwu, Uwakwe Joy Ijeoma. Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables. Int J Stat Distrib Appl. 2017;3(2):18-24. doi: 10.11648/j.ijsd.20170302.12

Copy | Download

@article{10.11648/j.ijsd.20170302.12,
  author = {Egbo Ikechukwu and Uwakwe Joy Ijeoma},
  title = {Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {3},
  number = {2},
  pages = {18-24},
  doi = {10.11648/j.ijsd.20170302.12},
  url = {https://doi.org/10.11648/j.ijsd.20170302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20170302.12},
  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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Asymptotic Performance of the Location and Logistic Classification Rules for Multivariate Binary Variables
AU  - Egbo Ikechukwu
AU  - Uwakwe Joy Ijeoma
Y1  - 2017/10/18
PY  - 2017
N1  - https://doi.org/10.11648/j.ijsd.20170302.12
DO  - 10.11648/j.ijsd.20170302.12
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 18
EP  - 24
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20170302.12
AB  - 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.
VL  - 3
IS  - 2
ER  -

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