ON ESTIMATION OF COVARIANCE MATRIX WITH MIXTURES OF CONTINUOUS AND CATEGORICAL VARIABLES
dc.contributor.author | Oyeyemi, G. M. | |
dc.contributor.author | Mbaeyi, G. C. | |
dc.date.accessioned | 2023-07-27T09:03:26Z | |
dc.date.available | 2023-07-27T09:03:26Z | |
dc.date.issued | 2017 | |
dc.description.abstract | A method known as the Location Model for discriminant analysis when discriminating variables are mixtures of continuous and categorical variables, which, unlike the conventional Linear Discriminant procedure does not assign arbitrary scores to each state of the categorical variable was studied. An alternative to estimating the covariance matrix for evaluating the discriminant function was suggested since the Location Model still makes such distortion of treating categorical variable as if they are continuous when estimating the covariance matrix. We compare the performance of the two procedures using the accuracy rate produced by each method under various conditions. Our suggested method performed better than the location model over all cases considered. | en_US |
dc.description.sponsorship | Self-sponsored | en_US |
dc.identifier.citation | Funai Journal of Science and Technology | en_US |
dc.identifier.uri | https://uilspace.unilorin.edu.ng/handle/20.500.12484/11633 | |
dc.language.iso | en | en_US |
dc.publisher | Faculty of Physical Sciences, Federal University of Ndufu-Alike-Ikwo, Nigeria. | en_US |
dc.relation.ispartofseries | 3(2);149 – 158 | |
dc.subject | Continuous, Categorical, Covariance matrix, Accuracy rate, Discriminant, Groups | en_US |
dc.title | ON ESTIMATION OF COVARIANCE MATRIX WITH MIXTURES OF CONTINUOUS AND CATEGORICAL VARIABLES | en_US |
dc.type | Article | en_US |
Files
Original bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- Paper 53.pdf
- Size:
- 545.68 KB
- Format:
- Adobe Portable Document Format
- Description:
- Main article
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed upon to submission
- Description: