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Maha Shabbir

Dr. Maha Shabbir is an Assistant Professor in the Department of Mathematics and Statistical Sciences. She has a PhD in Statistics from the College of Statistical Sciences, University of the Punjab, Lahore. Her research interests are statistical modelling, ridge regression, probability distribution, and regression analysis.

CV

  1. Shabbir, M., Chand, S., Iqbal, F., & Kisi, O. (2025). Novel Hybrid Approach for River Inflow Modeling: Case Study of the Indus River Basin, Pakistan. Journal of Hydrologic Engineering, 30(3), 04025006. https://doi.org/10.1061/JHYEFF.HEENG-6368
  2. Shabbir, M., Chand, S., & Dar, I. S. (2025). Bagging-based heteroscedasticity-adjusted ridge estimators in the linear regression model. Kuwait Journal of Science, 52(3), 100412. https://doi.org/10.1016/j.kjs.2025.100412
  3. Chand, S., & Shabbir, M. (2025). A new robust ridge estimator for linear regression model with non normal, heteroscedastic and autocorrelated errors. Communications in Statistics-Theory and Methods, 1-17. https://doi.org/10.1080/03610926.2025.2479640
  4. Shah, S. A. A., Zaman, Q., Wasim, D., Allohibi, J., Alharbi, A. A., & Shabbir, M. (2025). Optimal model for predicting highest runs chase outcomes in T-20 international cricket using modern classification algorithms. Alexandria Engineering Journal, 114, 588-598. https://doi.org/10.1016/j.aej.2024.11.113
  5. Wasim, D., Suhail, M., Khan, S. A., Shabbir, M., Awwad, F. A., Ismail, E. A., ... & Ali, A. (2025). Quantile-based robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliers: Real life applications using T20 cricket sports and anthropometric data. Kuwait Journal of Science, 52(1), 100336. https://doi.org/10.1016/j.kjs.2024.100336. 
  6. Shabbir, M., Chand, S., & Dar, I. S. (2025). Heteroscedastic-adjusted standard error based estimation of ridge parameter in the linear regression model. Communications in Statistics-Theory and Methods, 1-15. https://doi.org/10.1080/03610926.2025.2474633.
  7. Shabbir, M., Chand, S., & Iqbal, F. (2024). A novel hybrid approach based on outlier and error correction methods to predict river discharge using meteorological variables. Environmental and Ecological Statistics, 1-28. https://doi.org/10.1007/s10651-024-00628-4
  8. Zaman, Q., Wasim, D., Nawaz, S., & Shabbir, M. (2025). Modified robust ridge M-estimators to improve linear regression performance under multicollinearity and outliers. Journal of Statistical Computation and Simulation, 1-23. https://doi.org/10.1080/00949655.2025.2573859
  9. Wasim, D., Khan, S. A., Suhail, M., & Shabbir, M. (2025). New penalized M-estimators in robust ridge regression: real life applications using sports and tobacco data. Communications in Statistics-Simulation and Computation, 54(6), 1746-1765. https://doi.org/10.1080/03610918.2023.2293648
  10. Shabbir, M. (2025). A New Approach for the Estimation of ridge parameter in linear regression model with heteroscedastic errors. (Conference Paper)
  11. Dar, I. S., Chand, S., & Shabbir, M. (2025). An improved ridge-type estimator leveraging weighted least squares and horn’s scaling for heteroscedastic regression. Communications in Statistics-Theory and Methods, 1-20. https://doi.org/10.1080/03610926.2025.2535399.
  12. Wasim, D., Suhail, M., Albalawi, O., & Shabbir, M. (2024). Weighted penalized m-estimators in robust ridge regression: an application to gasoline consumption data. Journal of Statistical Computation and Simulation, 1-30. https://doi.org/10.1080/00949655.2024.2386391.
  13. Wasim, D., Khan, S.A., Bashir, A., & Shabbir, M. (2024). Statistical Study of Impact of Services on Balance of Payment in Pakistan. International Journal of Contemporary Issues in Social Sciences, 3(2), 2050-2057. https://ijciss.org/index.php/ijciss/article/view/920/1014
  14. Shabbir, M., Chand, S., & Iqbal, F. (2024). A novel hybrid framework to model the relationship of daily river discharge with meteorological variables. Meteorology Hydrology and Water Management. https://doi.org/10.26491/mhwm/187899
  15. Shabbir, M., Chand, S., Iqbal, F., & Kisi, O. (2024). Hybrid Approach for Streamflow Prediction: LASSO-Hampel Filter Integration with Support Vector Machines, Artificial Neural Networks, and Autoregressive Distributed Lag Models. Water Resources Management, 1-18. https://doi.org/10.1007/s11269-024-03858-0
  16. Shabbir, M., Chand, S., & Iqbal, F. (2024). Novel hybrid and weighted ensemble models to predict river discharge series with outliers. Kuwait Journal of Science, 51(2), 100188. https://doi.org/10.1016/j.kjs.2024.100188
  17. Wasim, D., Khan, S.A., Suhail, M., Shabbir, M. (2023). New Penalized M-estimators in robust ridge regression: Real life applications using Sports and Tobacco Data Communications in Statistics-Simulation and Computation. 1-20. https://doi.org/10.1080/03610918.2023.2293648
  18. Shabbir, M., Chand, S., & Iqbal, F. (2023). A new ridge estimator for linear regression model with some challenging behavior of error term. Communications in Statistics-Simulation and Computation, 1-11. https://doi.org/10.1080/03610918.2023.2186874.
  19. Shabbir, M., Chand, S., & Iqbal, F. (2023). Prediction of river inflow of the major tributaries of Indus river basin using hybrids of EEMD and LMD methods. Arabian Journal of Geosciences, 16(4), 257. https://doi.org/10.1007/s12517-023-11351-y.
  20. Shabbir, M., Chand, S., & Iqbal, F. (2023). A new hybrid model to predict streamflow. Published in the 6th International Researchers, Statisticians and Young Statisticians Congress (IRSYSC2022) Proceedings. Suleyman Demirel University, Isparta, Türkiye held on 03-06 November 2022. http://irsysc2022.com/files/IRSYSC2022_Proceeding_Book_v2.pdf
  21. Dar I.S., Chand, S., Shabbir, M., and Kibria B.M.G. (2022). Conditional-Index based New Ridge Regression Estimator for Linear Regression Model with Multicollinearity. Kuwait Journal of Science.1-12. https://doi.org/10.1016/j.kjs.2023.02.013.
  22. Shabbir, M., Chand, S., and Iqbal, F. (2022). Bagging-based ridge estimators for a linear regression model with non-normal and heteroscedastic errors. Communications in  Statistics-Simulation and Computation, 1-15. https://doi.org/10.1080/03610918.2022.2109675.
  23. Shabbir, M., Chand, S., and Iqbal, F. (2022). A Novel Hybrid Method for River Discharge Prediction. Water Resources Management, 36(1), 253-272. https://doi.org/10.1007/s11269-01-03026-8.
  24. Riaz, A., Akhter, A.S., and Shabbir, M. (2019). INVERSE EXPONENTIAL LOMAX DISTRIBUTION: PROPERTIES AND APPLICATION. In 15th Islamic Countries Conference on Statistical Sciences (ICCS-15) (p. 157).
  25. Shabbir, M., Riaz, A., and Gull, H. (2018). Rayleigh Lomax Distribution. The Journal of Middle East and North Africa Sciences, 4(12), 1-4.
  26. Shabbir, M., Noor, N., Riaz, A., and Gull, H. (2017). The New Weibull Lomax Distribution. Imperial Journal of Interdisciplinary Research, 3(1), 1881-1885.