Proposed Hybrid Method for Wavelet Shrinkage with Robust Multiple Linear Regression Model : With Simulation Study

Taha Hussien Ali; Dlshad Mahmood Saleh

doi:10.25212/lfu.qzj.7.1.36

Authors

Taha Hussien Ali Department of Statistics and Informatics, College of Administration and Economics, University of Salahaddin, Erbil, Iraq.
Dlshad Mahmood Saleh Department of Statistics and Informatics, College of Administration and Economics, University of Salahaddin, Erbil, Iraq.

DOI:

https://doi.org/10.25212/lfu.qzj.7.1.36

Keywords:

Linear Regression Model; Robust Method; Wavelet; Shrinkage

Abstract

This study compares the proposed hybrid method (wavelet robust M-estimation) to the traditional method (wavelet ordinary least square) when there are de-noising or outlier problems for estimating multiple linear regression models using the statistical criterion root mean square error (RMSE). According to simulated and real data, the proposed hybrid method (wavelet robust M-estimation) is better than the classical method (Wavelet Ordinary Least Square) and more accurate. The root mean square error of the proposed hybrid method (wavelet robust M-estimation) is less than the Wavelet Ordinary Least Square. Therefore, it is recommended to use the hybrid proposed method to reduce the problem of outliers and de-noise data.

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