Estimation of the Regression Model Using M-Estimation Method and Artificial Neural Networks in the Presence of Outliers
DOI:
https://doi.org/10.33095/g4hems75Keywords:
Regression, M-Estimation, Artificial Neural Networks, Outlier, Robust Regression, Activation Function.Abstract
This study aimed to predict using regression models in the presence of outliers in the study data. The research delved into outliers, their detection, and model estimation through robust methods, represented by the M estimator and multilayer artificial neural networks. A comparison between these methods was conducted, and they were applied to real data representing a survey of private-sector power generators for 2021. Model evaluation was performed using Mean Squared Error (MSE) and the determination coefficient. The results indicated that the M-estimator with the Huber function outperformed its counterpart with the Tukey function. The best-performing architecture for the artificial neural network was ML-FF (5, 16, 32, 46, 128, 1) with the relu activation function. This network effectively handled extreme values and exhibited strong predictive capabilities. The choice of activation function and the number of hidden layers significantly impacted the neural network's performance, with the results showing the superiority of this artificial neural network over the robust estimators.
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