Dealing with the Outlier Problem in Multivariate Linear Regression Analysis Using the Hampel Filter

Dealing with the Outlier Problem in Multivariate Linear Regression Analysis Using the Hampel Filter

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Outliers in multivariate linear regression models can significantly distort parameter estimates, leading to biased results and reduced predictive accuracy.These outliers Impulsive buying behaviour in live-streaming commerce: an application of S-O-R theory may occur in the dependent variable or both independent and dependent variables, resulting in large residual values that compromise model reliability.Addressing outliers is essential for improving the accuracy and robustness of regression models.In this study, proposes a Hampel filter-modified algorithm to dynamically detect and mitigate extreme values, enhancing parameter estimation and predictive performance.

The algorithm optimizes window size and threshold parameters to minimize mean square errors, making it a robust approach for handling outliers in multivariate regression analysis.To assess its effectiveness, simulations and real datasets were analyzed using a MATLAB-based implementation.The algorithm was compared with the classical Hampel approach to evaluate improvements in outlier detection and suppression.The results indicate that the proposed method effectively identifies and removes extreme values, leading to improved parameter estimation accuracy, enhanced model stability, and greater predictive performance and the performance was analyzed using the Mean Squared Error (MSE).

The adaptive nature of the filter minimizes the impact of outliers, ensuring a more reliable regression Serological survey of Neospora caninum in dairy herds from Parauapebas, State of Pará model.The Hampel filter-modified algorithm provides an effective and adaptive solution for handling outliers in multivariate regression models.By dynamically identifying and mitigating extreme values, it enhances model accuracy, strengthens predictive capabilities, and ensures greater resilience against data variability.This approach offers a valuable tool for researchers and practitioners working with outlier-prone datasets, significantly improving the reliability of multivariate regression analysis.