Robust-M new two-parameter estimator for linear regression models: Simulations and applications
In the presence of multicollinearity and outliers, the ordinary least squares estimator remains inconsistent and unreliable. Several estimators have been proposed that can co-handle the problems of multicollinearity and outliers simultaneously. However, there is still a need to explore some other ro...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nigerian Society of Physical Sciences
2023-11-01
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| Series: | African Scientific Reports |
| Subjects: | |
| Online Access: | https://asr.nsps.org.ng/index.php/asr/article/view/138 |
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| author | Taiwo Joel Adejumo K. Ayinde A. A. Akomolafe O. S. Makinde A. S. Ajiboye |
| author_facet | Taiwo Joel Adejumo K. Ayinde A. A. Akomolafe O. S. Makinde A. S. Ajiboye |
| author_sort | Taiwo Joel Adejumo |
| collection | DOAJ |
| description | In the presence of multicollinearity and outliers, the ordinary least squares estimator remains inconsistent and unreliable. Several estimators have been proposed that can co-handle the problems of multicollinearity and outliers simultaneously. However, there is still a need to explore some other robust methods when the two anomalies appear in the linear regression model and recommend it to end users of statistics. Therefore, this study proposed Robust-M New Two Parameter (RNTP) and examined its performance over some already existing ones in the presence of multicollinearity and outliers in the x-direction. The theoretical expression under some conditions was established to showcase the new estimator's superiority. A simulation study was carried out alongside some factors to show that the RNTP is better than all other estimators considered in the study. The simulation study results revealed that RNTP outperformed other estimators in the study using the minimum MSE as the criterion. Likewise, real-life data was applied to affirm this claim.
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| format | Article |
| id | doaj-art-4bb9ae6dcad04517a8183685816b2cf9 |
| institution | Kabale University |
| issn | 2955-1625 2955-1617 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | Nigerian Society of Physical Sciences |
| record_format | Article |
| series | African Scientific Reports |
| spelling | doaj-art-4bb9ae6dcad04517a8183685816b2cf92025-08-20T03:31:34ZengNigerian Society of Physical SciencesAfrican Scientific Reports2955-16252955-16172023-11-012310.46481/asr.2023.2.3.138138Robust-M new two-parameter estimator for linear regression models: Simulations and applicationsTaiwo Joel AdejumoK. AyindeA. A. AkomolafeO. S. MakindeA. S. AjiboyeIn the presence of multicollinearity and outliers, the ordinary least squares estimator remains inconsistent and unreliable. Several estimators have been proposed that can co-handle the problems of multicollinearity and outliers simultaneously. However, there is still a need to explore some other robust methods when the two anomalies appear in the linear regression model and recommend it to end users of statistics. Therefore, this study proposed Robust-M New Two Parameter (RNTP) and examined its performance over some already existing ones in the presence of multicollinearity and outliers in the x-direction. The theoretical expression under some conditions was established to showcase the new estimator's superiority. A simulation study was carried out alongside some factors to show that the RNTP is better than all other estimators considered in the study. The simulation study results revealed that RNTP outperformed other estimators in the study using the minimum MSE as the criterion. Likewise, real-life data was applied to affirm this claim. https://asr.nsps.org.ng/index.php/asr/article/view/138Ordinary least squaresMulticollinearityOutliersEstimatorsSimulation study |
| spellingShingle | Taiwo Joel Adejumo K. Ayinde A. A. Akomolafe O. S. Makinde A. S. Ajiboye Robust-M new two-parameter estimator for linear regression models: Simulations and applications African Scientific Reports Ordinary least squares Multicollinearity Outliers Estimators Simulation study |
| title | Robust-M new two-parameter estimator for linear regression models: Simulations and applications |
| title_full | Robust-M new two-parameter estimator for linear regression models: Simulations and applications |
| title_fullStr | Robust-M new two-parameter estimator for linear regression models: Simulations and applications |
| title_full_unstemmed | Robust-M new two-parameter estimator for linear regression models: Simulations and applications |
| title_short | Robust-M new two-parameter estimator for linear regression models: Simulations and applications |
| title_sort | robust m new two parameter estimator for linear regression models simulations and applications |
| topic | Ordinary least squares Multicollinearity Outliers Estimators Simulation study |
| url | https://asr.nsps.org.ng/index.php/asr/article/view/138 |
| work_keys_str_mv | AT taiwojoeladejumo robustmnewtwoparameterestimatorforlinearregressionmodelssimulationsandapplications AT kayinde robustmnewtwoparameterestimatorforlinearregressionmodelssimulationsandapplications AT aaakomolafe robustmnewtwoparameterestimatorforlinearregressionmodelssimulationsandapplications AT osmakinde robustmnewtwoparameterestimatorforlinearregressionmodelssimulationsandapplications AT asajiboye robustmnewtwoparameterestimatorforlinearregressionmodelssimulationsandapplications |