International Journal of Mathematics Trends and Technology
Volume 30 | Number 1 | Year 2016 | Article Id. IJMTT-V30P509 | DOI : https://doi.org/10.14445/22315373/IJMTT-V30P509
This paper presents a modification to ordinary least squares (OLS) method with a view to overcoming the ill-effects of collinearity on the OLS estimates of the regression parameters in a linear model with two explanatory variables. This modified approach leads to estimates that are, to a large extent, better than OLS estimates under the mean square error criterion and also overcome the overestimation problem that plagues the OLS estimates. Although a few attempts to get improved estimates have been made by some authors, the method developed here takes a route that has not been hitherto ventured in the context of addressing collinearity issues.
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Martin L. William, L.Maria Alphonse Ligori, "A Modified Least-Squares Approach to Mitigate the Effect of Collinearity in Two- Variable Regression Models," International Journal of Mathematics Trends and Technology (IJMTT), vol. 30, no. 1, pp. 48-52, 2016. Crossref, https://doi.org/10.14445/22315373/IJMTT-V30P509
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