Ugah, Tobias Ejiofor and Mba, Emmanuel Ikechukwu and Eze, Micheal Chinonso and Arum, Kingsley Chinedu and Mba, Ifeoma Christy and Urama, Chinasa E. and Ekene-Okafor, Comfort Njideka (2021) An Asymptotic Test for A Single Outlier in Linear Regression Models. Mathematics and Statitics, 9 (6). pp. 1004-1010.
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Abstract
It is not uncommon to find an outlier in the response variable in linear regression. Such a deviant value needs to be detected and scrutinized to find out why it is not in agreement with its fitted value. Srikantan [1] has developed a test statistic for detecting the presence of an outlier in the response variable in a multiple linear regression model. Approximate critical values of this test statistic are available and are obtained based on the first-order Bonferroni upper bound. The exact critical values are not available and a result of that, tests carried out on the basis of this approximate critical values may not be very accurate. In this paper, we obtained more accurate and precise critical values of this test statistic for large sample sizes (herein called asymptotic critical values) to improve on the tests that use these critical values. The procedure involved using the exact probability density function of this test statistic to obtain its asymptotic critical values. We then compared these asymptotic critical values with the approximate critical values obtained. An application to simulation results for linear regression models was used to examine the power of this test statistic. The asymptotic critical values obtained were found to be more accurate and precise. Also, the test performed better under these asymptotic values (the power performance of this test statistic was found to better when the asymptotic critical values were used)
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Chemistry |
| Depositing User: | Cynthia Ugwuoti |
| Date Deposited: | 30 May 2025 13:31 |
| Last Modified: | 30 May 2025 13:31 |
| URI: | http://eprints.gouni.edu.ng/id/eprint/4697 |
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