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Assessing the Performance of Ordinary Least Square and Kernel Regression
2020
Zenodo
The assessment of Ordinary Least Squares (OLS) and kernel regression on their predictive performance was studied. We used simulated data to assess the performance of estimators using small and large sample. However, the mean square error (MSE) and root mean square error (RMSE) was used to find out the most efficient among the estimated models. The results show that, when the ordinary least square is more efficient than the kernel regression due to having the least MSE and RMSE in both
doi:10.5281/zenodo.3764305
fatcat:wugyrmd27ndybiz4outzvwdj4u