Aduol, F.W. (2003). Robust geodetic parameter estimation under least squares through weighting based on the mean square error. In Geodesy-the Challenge of the 3rd Millennium (pp. 269-276). Berlin, Heidelberg: Springer Berlin Heidelberg.

Ali, Taha Hussein & Awaz Shahab M.(2017) "Uses of Waveshrink in Detection and Treatment of Outlier Values in Linear Regression Analysis and Comparison with Some Robust Methods", Journal of Humanity Sciences 21.5 ,pp 38-61.

Ali, T. H., and Saleh, D.M. (2021). Comparison Between Wavelet Bayesian and Bayesian Estimators to Remedy Contamination in Linear Regression Model, PalArch's Journal of Archaeology of Egypt/Egyptology 18.10 ,pp 3388-3409

Belsley, D.A., Kuh, E. and Welsch, R.E. (2004). Regression Diagnostics; Identifying Influence Data and Source of Collinearity. Wiley, New York.

Chen, R. and Paschalidis, I.C., (2018). A robust learning approach for regression models based on distributionally robust optimization. Journal of Machine Learning Research, 19(13), pp.1-48.

Cohen,J., Cohen,P., West, S.G. & Aiken, L.S., (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Third edition. Routledge.

Doane, D.P. & Seward, L., 2021. Applied Statistics in Business and Economics. McGraw-Hill Education.

Gábor, A. and Banga, J.R., 2015. Robust and efficient parameter estimation in dynamic models of biological systems. BMC Systems Biology, 9, pp.1-25.

Ismael, A. A. & Albairmani, Z. A. (2021). The effect of some independent variables on granting the basic application certificate using multivariate statistical analysis ,Tikrit Journal of Administrative and Economic Sciences, Vol. 17, No. 54,part 3, 509-522

Ipek, D. K., Keser I. K. (2023) Outlier detection based on the functional coefficient of variation STATISTICS IN TRANSITION Vol. 24, No. 2, pp. 1–16,

Kotlyarova, Y. and Zinde-Walsh, V., 2009. Robust estimation in binary choice models. Communications in Statistics-Theory and Methods, 39(2), pp.266-279.

Mahmood. S. H. (2024). Estimating Models and Evaluating their Efficiency under Multicollinearity in Multiple Linear Regression: A Comparative Study, ZANCO Journal of Humanity Sciences, 28(5) , 264 – 277

Omer, A. W., Sedeeq, B. S., & Ali, T. H. (2024). A proposed hybrid method for Multivariate Linear Regression Model and Multivariate Wavelets (Simulation study). Polytechnic Journal of Humanities and Social Sciences, 5(1), 112-124.

Rawlings, O., Pantula, G. & Dickey, D.A., 1998. Applied Regression Analysis: A Research Tool, 2nd Edition. Springer.

Rencher, A.C. & Schaalje, G., (2008). Linear models in statistics. 2nd ed. John Wiley & Sons.

Rousseeuw, P.J. and Leroy, A.M. (1987). Robust Regression and Outlier Detection. Wiley Series in Probability and Mathematical Statistics, John Wiley, New York.

Sedeeq, B.S., Qader, H.M., Aziz, A.A., Saleh, D .M. (2023), Implementing a New Scale Technique in the M-Estimation Method to Estimate Parameters of Multiple Linear Regression: Simulation Study ,Tikrit Journal of Administrative and Economic Sciences, Vol. 19, No. 64, Part (1): 712-725, Doi:10.25130/tjaes.19.64.1.38

Seber, G.A.F. and Lee, A.J. (2003). Linear Regression Analysis, Second Edition. John Wiley & Sons, Inc., Hoboken, New York

Wilcox, R.R., 2011. Introduction to robust estimation and hypothesis testing. Academic press.

Yonekura, S. and Sugasawa, S., 2023. Adaptation of the tuning parameter in general Bayesian inference with robust divergence. Statistics and Computing, 33(2), 1-16.