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Dr. Muhammad Amin has done Ph.D. & M. Phill in Statistics from Bahauddin Zakariya University (BZU), Multan, Pakistan. He is currently working as a Assistant Professor (HEC Approved Supervisor). He was the Incharge of the Statistics department, university of Sargodha from May 31, 2019-December 13, 2021. Previously, he served as a lecturer in Statistics at Govt. College for boys Makhdoom Aali, District Lodhran, Pakistan.

Research Summary
Total Research Papers=82, Total Citations=840, h-index=17, i10-index=27, Total Impact Factor=116.266

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Education

Year Degree Board / University
2016 Doctor of Philosophy in the Subject of Statistics -- (Doctorate Degree) Bahauddin Zakariya University, Multan
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Experience

From To Post Held Organization
2006 2008 Six Sigma Executive MDS Mills Ltd, Multan
2009 2018 Lecturer HED
2018 Continue Assistant Professor University of Sargodha
2019 2021 Incharge University of Sargodha
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Research Interests

Generalized Linear models
Influence Diagnostics
Biased Estimators
Applied Statistics

Publications

    Research Journal


  • A. Batool, M. Amin, A. Elhassanein , 2023 , On the performance of some new ridge parameter estimators in the Poisson-inverse Gaussian ridge regression , Alexandria Engineering Journal , 70, IF: 6.626
  • M. Cheema, M. Amin, T. Mahmood, M. Faisal, K. Brahim, A. Elhassanein , 2023 , Deviance and Pearson residuals-based control charts with different link functions for monitoring logistic regression profiles: An application to COVID-19 Data , Mathematics , 11, 5, IF: 2.592
  • M. Amin, MN, Akram, BMG. Kibria, HM. Alshanbari, N. Fatima, A. Elhassanein. , 2023 , On the Estimation of the Binary Response Model , Axioms , 12, 2, IF: 1.824
  • AF. Lukman, BMG. Kibria, CK. Nziku, M. Amin, ET. Adewuyi, R. Farghali , 2023 , K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model. , Mathematics , 11, 2, IF: 2.592
  • Amin, M., Afzal, S., Akram, M.N., Muse, A.H., Tolba, A.H. , 2022 , Outlier detection in gamma regression using Pearson residuals: Simulation and Application , AIMS Mathematics , 7, 8, IF: 2.739
  • Hayat, A., Amin, M., Afzal, S., Muse, A.H., Egeh, O.M., Hayat, H.S. , 2022 , Application of Regression Analysis to Identify the Soil and Other Factors Affecting the Wheat Yield. , Advances in Materials Science and Engineering , 2022, IF: 2.098
  • Yasin, A., Amin, M., Qasim, M., Muse, A.H., Soliman, A.B. , 2022 , More on the Ridge Parameter Estimators for the Gamma Ridge Regression Model: Simulation and Applications , Mathematical Problems in Engineering , 2022, IF: 1.43
  • Mustafa, S., Amin, M., Akram, M.N., Afzal, N. , 2022 , On the performance of some link functions in the beta ridge regression model: Simulation and Application , Concurrency and Computation: Practice and Experience , 34, 18, IF: 1.831
  • Akram, M.N., Amin, M., Sami, F., Mastor, A.B., Egeh, O.M., Muse, A.H. , 2022 , A New Conway Maxwell–Poisson Liu Regression Estimator—Method and Application , Journal of Mathematics , 2022, IF: 1.555
  • Sami, F., Amin, M., Akram, M.N., Butt, M.M., Ashraf, B. , 2022 , A modified one parameter Liu estimator for Conway-Maxwell Poisson response model , Journal of Statistical Computation and Simulation , 92, 12, IF: 1.225
  • MN. Akram, M. Amin, MA. Ullah, A. Afzal , 2021 , Modified ridge-type estimator for the inverse gaussian regression model , Communications in Statistics - Theory and Methods , 52, 10, IF: 0.863
  • S. Saleem, R.A.K. Sherwani, M. Amin , 2021 , Development of a new modified hogg type adaptive scheme for multilevel models with diverse error distributions , Communications in Statistics-Theory and Methods , 52, 8, IF: 0.863
  • M. Amin, M.N. Akram, A. Majid , 2021 , On the estimation of Bell regression model using ridge estimator , Communications in Statistics - Simulation and Computation , 52, 3, IF: 1.162
  • Amin, M., Akram, M.N. and Qasim, R. , 2020 , Bayesian estimation of ridge parameter under different loss functions. , Communications in Statistics - Theory and Methods , 51, 12, IF: 0.863
  • Amin, M., Amanullah, M. and Qasim, M. , 2020 , Diagnostic techniques for the inverse Gaussian regression model. , Communications in Statistics - Theory and Methods , 51, 8, IF: 0.863
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