Ali, T. H., Sedeeq, B. S., Saleh, D. M., & Rahim, A. G. (2024). Robust multivariate quality control charts for enhanced variability monitoring. Quality and Reliability Engineering International, 40(3), 1369-1381. https://doi.org/10.1002/qre.3472.

Ali, T. H., & 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, 38-61.

Ali, T., Albarwari, N. H. S., & Ramadhan, D. L. (2023). Using the hybrid proposed method for Quantile Regression and Multivariate Wavelet in estimating the linear model parameters. Iraqi Journal of Statistical Sciences, 20(1), 9-24.

Ali, T., Al-Saffar, A., & Ismael, S. S. (2023). Using Bayes weights to estimate parameters of a Gamma Regression model. Iraqi Journal of Statistical Sciences, 20(1), 43-54.

Ali, T. H., Mahmood, A. P. D. S. H., & Wahdi, A. P. D. A. S. (2022). Using Proposed Hybrid method for neural networks and wavelet to estimate time series model. Tikrit Journal of Administrative and Economic Sciences, 18(57 part 3).

Buylov, V. (2022). Practical application of econometric modelling to market approach method using Tesla Motors, Inc. as an example.

da Silva Chagas, C., de Carvalho Junior, W., Bhering, S. B., & Calderano Filho, B. (2016). Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions. Catena, 139, 232-240.

Gladwell, M. (2008). Outliers: The story of success. Little, Brown.

Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust Statistics: The Approach Based on Influence Functions. John Wiley & Sons.

Huber, P. J. (1992). Robust estimation of a location parameter. In Breakthroughs in statistics: Methodology and distribution (pp. 492-518). New York, NY: Springer New York.

Huber, P. J. (1981). Robust Statistics. John Wiley & Sons.

Huber, P. J., & Ronchetti, E. M. (2009). Robust Statistics, John Wiley & Sons, Inc. Publication.

Kareem, N.S., Ali, T. H., and Mohammad, A. S. (2019) "Construction robust simple linear regression profile Monitoring" Journal of Kirkuk University for Administrative and Economic Sciences, 9.1.: 242-257.

Maronna, R. A., Martin, R. D., & Yohai, V. J. (2006). Robust Statistics: Theory and Methods. John Wiley & Sons.

Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R). John Wiley & Sons.

McLean, K. A., Wu, S., & McAuley, K. B. (2012). Mean-squared-error methods for selecting optimal parameter subsets for estimation. Industrial & Engineering Chemistry Research, 51(17), 6105-6115.

Omar, C., & Ali, T. (2020). Using Bayes weights to remedy the heterogeneity problem of random error variance in linear models. Iraqi Journal of Statistical Sciences, 17(2), 58-67.

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.

Pregibon, D. (1982). Resistant fits for some commonly used logistic models with medical applications. Biometrics, 38(2), 485-498.

Wang, Y. G., & Lin, X. (2005). Effects of variance-function misspecification in analysis of longitudinal data. Biometrics, 61(2), 413-421.

Xie, X., Wu, T., Zhu, M., Jiang, G., Xu, Y., Wang, X., & Pu, L. (2021). Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecological Indicators, 120, 106925.‏

Yan, X., & Su, X. (2009). Linear regression analysis: theory and computing. world scientific.

Zhang, H., Wu, P., Yin, A., Yang, X., Zhang, M., & Gao, C. (2017). Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model. Science of the Total Environment, 592, 704-713.‏