Robust Linear Regression Estimation via Symlet Andrews Weighted Function for Outlier Detection
DOI:
https://doi.org/10.24086/cuesj.v10n1y2026.pp80-86Keywords:
symlet wavelet, Andrews weighted function, Robust estimation, Outliers, Linear regression.Abstract
This paper suggests a novel method for robust estimation in linear regression models by fusing the Andrews weighted function with Symlet wavelet analysis. The result is a new weighted function that includes the Symlet Andrews Weighted Function. These functions try to reduce the effect of outliers in regression analysis by giving data points with high residuals lower weights. The effectiveness of these new weighted functions was evaluated using simulated experiments, using the root mean square error (RMSE) as the standard. The results show that, in terms of RMSE values, the recommended weighted function consistently performs better than the traditional weighted function. This improvement suggests that because the new function can handle outlier data, they are more reliable for linear regression analysis, including problematic data.
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Copyright (c) 2026 Hawkar Q. Birdawod, Zewar O. Ismael, Dlshad M. Saleh, Dashty I. Jamil
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