Comparison of Wavelet Shrinkage and Hampel Filter in the Analysis of Multivariate Linear Regression Models

Section: Article
Issue
Vol. 22 No. 2 (2025): Volume 22 Issue 2
Published
Nov 30, 2025
Pages
1-16

Abstract

The presence of outliers in the data of a multivariate regression model affects the accuracy of the estimated model parameters and leads to unacceptably large residual values. Therefore, some filters, including the Hempel filter, are usually used to handle outliers (or use some robust method). This paper proposes to employ wavelet shrinkage to address the problem of outliers in multivariate regression model data by using wavelets (Coiflets, Daubechies, and Demy) with a universal threshold method and soft rule. To illustrate the efficiency of the proposed method (Wavelet Shrinkage filter) was compared with the traditional method (Hampel filter) based on the mean square error criterion through simulation and real data. A program has been designed in MATLAB to do this. The results proved that the Wavelet shrinkage filter method was more efficient than the traditional method in dealing with the outlier problem and obtaining more accurate multivariate model parameters than the Hampel filter method.

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Authors

Amira Wali Omer

Statistics and Informatics Department- College of Administration & Economics - Salahaddin University – Erbil, Iraq

Taha Hussein Ali

Statistics and Informatics Department- College of Administration & Economics - Salahaddin University – Erbil, Iraq

Identifiers

https://doi.org/10.33899/iqjoss.v22i2.54068
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How to Cite

Omer, A. W. ., & Ali, T. H. . (2025). Comparison of Wavelet Shrinkage and Hampel Filter in the Analysis of Multivariate Linear Regression Models. IRAQI JOURNAL OF STATISTICAL SCIENCES, 22(2), 1–16. https://doi.org/10.33899/iqjoss.v22i2.54068