Discriminating Between Ordinary Least Squares Estimation Method and Some Robust Estimation Regression Methods

Authors: ,

Abstract: The lack of certain assumptions is common in ordinary least squares regression models whenever there is/are outliers and high leverage in the observations with an extreme value on a predictor variable. This could have a great effect on the estimate of regression coefficients. However, this research investigates the performance of the ordinary least squares estimator method and some robust regression methods which include: M-Huber, M-Bisquare, MM, and M-Hampel estimator methods. This study applies both methods to a secondary data set with 28 years (from 1900 to 2021) 200 meter races Summer Olympic Games with a response variable (sprint time) and three predictor variables (age, weight, and height) for illustration. Also, linearity, homoscedasticity, independence, and normality assumptions based on diagnostics regression like residual, normal Q-Q, scale-location, and cook’s distance were checked. Then, the results obtained show that the robust regression methods are more efficient than the ordinary least square estimator method.

International Journal of Computational and Applied Mathematics & Computer Science, E-ISSN: 2769-2477, Volume 3, 2023, Art. #9

PDF
DOI
XML
Cite
×
Citation Tools
WSEAS AMA
Badmus Nofiu Idowu, Ogundeji Rotimi Kayode, "Discriminating Between Ordinary Least Squares Estimation Method and Some Robust Estimation Regression Methods," International Journal of Computational and Applied Mathematics & Computer Science, vol. 3, pp. 72-79, 2023, DOI:10.37394/232028.2023.3.9
Badmus Nofiu Idowu, Ogundeji Rotimi Kayode. Discriminating Between Ordinary Least Squares Estimation Method and Some Robust Estimation Regression Methods. International Journal of Computational and Applied Mathematics & Computer Science. 2023;3:72-79. 10.37394/232028.2023.3.9
Copy to Clipboard
Citation copied to Clipboard

Certification

---