Browsing by Author "Alanamu, T"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item A New Robust Method for Estimating Linear Regression Model in the Presence of Outliers(Akamai University, U.S.A, 2018) Alanamu, T; Oyeyemi, G. M.Ordinary Least-Squares (OLS) estimators for a linear model are very sensitive to unusual values in the design space or outliers among response values. Even single atypical value may have a large effect on the parameter estimates. In this paper, we propose a new class of robust regression method for the classical linear regression model. The proposed method was developed using regularization methods that allow one to handle a variety of inferential problems where there are more covariates than cases. Specifically, each outlying point in the data is estimated using case-specific parameter. Penalized estimators are often suggested when the number of parameters in the model is more than the number of observed data points. In light of this, we propose the use of Ridge regression method for estimating the case-specific parameters. The proposed robust regression method was validated using Monte-Carlo datasets of varying proportion of outliers. Also, performance comparison was done for the proposed method with some existing robust methods. Assessment criteria results using breakdown point and efficiency revealed the supremacy of the proposed method over the existing methods considered.Item REVIEW OF SOME ROBUST ESTIMATORS IN MULTIPLE LINEAR REGRESSIONS IN THE PRESENCE OF OUTLIER(s)(African Journal of Mathematics and Statistics Studies, 2023) Alanamu, T; Oyeyemi, G. M.; Olaniran, O. R.; Adetunji, K. O.Linear regression has been one of the most important statistical data analysis tools. Multiple regression is a type of regression where the dependent variable shows a linear relationship with two or more independent variables. OLS estimate is extremely sensitive to unusual observations (outliers), with low breakdown point and low efficiency. This paper reviews and compares some of the existing robust methods (Least Absolute Deviation, Huber M Estimator, Bisquare M Estimator, MM Estimator, Least Median Square, Least Trimmed Square, S Estimator); a simulation method is used to compare the selected existing methods. It was concluded based on the results that for y direction outlier, the best estimator in terms of high efficiency and breakdown point of at most 0.3 is MM; for x direction outlier, the best estimator in term breakdown point of at most 0.4 is S; for x, y direction outlier, the best estimator in terms of high effici ency and breakdown point of at most 0.2 is MM.