Dr. A.M. Elsawah

Division of Science and Technology
Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China


Highest Degree
PostDoc. in Experimental Statistics from Hong Kong Baptist University, China

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Area of Interest:

Bayesian Inference
Experimental Designs
Machine Learning
Time Series Analysis
High Dimensional Data

Selected Publications

  1. Weng, L.C., A.M. Elsawah and K.T. Fang, 2021. Cross-entropy loss for recommending efficient fold-over technique. J. Syst. Sci. Complexity, 10.1007/s11424-020-9267-9.
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  2. Elsawah, A.M., 2021. Multiple doubling: a simple effective construction technique for optimal two-level experimental designs. Stat. Papers, 10.1007/s00362-020-01221-0.
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  3. Elsawah, A.M., 2021. An appealing technique for designing optimal large experiments with three-level factors. J. Comput. Appl. Math., 10.1016/j.cam.2020.113164.
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  4. Vishwakarma, G.K., C. Paul and A.M. Elsawah, 2020. An algorithm for outlier detection in a time series model using backpropagation neural network. J. King Saud Uni. - Sci., 10.1016/j.jksus.2020.09.018.
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  5. Ke, X., K.T. Fang, A.M. Elsawah and Y. Lin, 2020. New non-isomorphic detection methods for orthogonal designs. Commun. Stat. - Simul. Computation, 10.1080/03610918.2020.1844895.
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  6. Elsawah A.M., 2020. Building some bridges among various experimental designs. Journal of the Korean Statistical Society 49: 55-81.
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  7. Elsawah, A.M., Y. Tang and K.T. Fang, 2019. Constructing optimal projection designs. Statistics, 53: 1357-1385.
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  8. Elsawah, A.M., K.T. Fang, P. He and H. Qin, 2019. Sharp lower bounds of various uniformity criteria for constructing uniform designs. Stat. Papers, 10.1007/s00362-019-01143-6.
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  9. Elsawah, A.M., K.T. Fang and Y.H. Deng, 2019. Some interesting behaviors of good lattice point sets. Commun. Stat. Simul. Comput., 10.1080/03610918.2019.1628988.
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  10. Elsawah, A.M., K.T. Fang and X. Ke, 2019. New recommended designs for screening either qualitative or quantitative factors. Stat. Papers, 10.1007/s00362-019-01089-9.
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  11. Elsawah, A.M., 2019. Designing uniform computer sequential experiments with mixture levels using lee discrepancy. J. Syst. Sci. Complexity, 32: 681-708.
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  12. IOP Publishing Ltd., 2019. Conference on Sustainability Science 2018 IOP Conference Series: Earth and Environmental Science, 2018 9–10, October, IOP Publishing, pp-.
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  13. Elsawah, A.M., F. Essawe and H. Zhao, 2018. Asymptotic theory of dual generalized order statistics from heterogeneous population. J. Indian Soc. Probab. Stat., 19: 359-377.
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  14. Elsawah, A.M. and K.T. Fang, 2018. New results on quaternary codes and their Gray map images for constructing uniform designs. Metrika, 81: 307-336.
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  15. Elsawah, A.M. and K.T. Fang, 2018. A catalog of optimal foldover plans for constructing U-uniform minimum aberration four-level combined designs. J. Appl. Stat. 46: 1288-1322.
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  16. Fang, K.T., X. Ke and A.M. Elsawah, 2017. Construction of uniform designs via an adjusted threshold accepting algorithm. J. Complexity, 43: 28-37.
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  17. Elsawah, A.M., K.T. Fang, P. He and H. Qin, 2017. Optimum addition of information to computer experiments in view of uniformity and orthogonality. Bull. Malays. Math. Sci. Soc., 42: 803-826.
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  18. Elsawah, A.M., J. Hu and H. Qin, 2017. Effective lower bounds of wrap-around L2-discrepancy on three-level combined designs. J. Sys. Sci. Complex, 30: 1459-1469.
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  19. Elsawah, A.M., 2017. Constructing optimal router bit life sequential experimental designs: new results with a case study. Commun. Stat. Simul. Comput., 48: 723-752.
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  20. Elsawah, A.M., 2017. Choice of optimal second stage designs in two-stage experiments. Comput. Stat., 33: 933-965.
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  21. Elsawah, A.M., 2017. A powerful and efficient algorithm for breaking the links between aliased effects in asymmetric designs. Aust. N. Z. J. Stat., 59: 17-41.
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  22. Elsawah, A.M., M.A. Al-Awady, M.A.A. Elgawad and H. Qin, 2016. A note on optimal foldover four-level factorials. Acta. Math. Sin.-English Ser., 32: 286-296.
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  23. Elsawah, A.M., H. Qin, 2016. Optimum mechanism for breaking the confounding effects of mixed-level designs. Comput. Stat., 32: 781-802.
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  24. Elsawah, A.M., 2016. Constructing optimal asymmetric combined designs via Lee discrepancy. Stat. Probab. Lett., 118: 24-31.
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  25. Elsawah, A.M., 2016. A closer look at de-aliasing effects using an efficient foldover technique. Statistics, 51: 532-557.
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  26. Elsawah, A.M. and H. Qin, 2016. Asymmetric uniform designs based on mixture discrepancy. J. Appl. Stat., 43: 2280-2294.
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  27. Elsawah, A.M. and H. Qin, 2016. An effective approach for the optimum addition of runs to three-level uniform designs. J. Korean Stat. Soc., 45: 610-622.
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  28. Elsawah, A.M. and H. Qin, 2016. A new look on optimal foldover plans in terms of uniformity criteria. Commun. Stat.- Theory Methods, 46: 1621-1635.
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  29. Alawady, M.A., A.M. Elsawah, J. Hu and H. Qin, 2016. Asymptotic random extremal ratio and product based on generalized order statistics and its dual. Commun. Stat.- Theory Methods, 46: 8881-8896.
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  30. Elsawah, A.M. and H. Qin, 2015. Mixture discrepancy on symmetric balanced designs. Stat. Probab. Lett., 104: 123-132.
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  31. Elsawah, A.M. and H. Qin, 2015. Lower bound of centered L2-discrepancy for mixed two and three levels U-type designs, J. Stat. Plann. Inference, 161: 1-11.
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  32. Elsawah, A.M. and H. Qin, 2015. An efficient methodology for constructing optimal foldover designs in terms of mixture discrepancy. J. Korean Stat. Soc., 45: 77-88.
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  33. Elsawah, A.M. and H. Qin, 2015. A new strategy for optimal foldover two-level designs. Stat. Probab. Lett., 103: 116-126.
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  34. Barakat, H.M., E.M. Nigm and A.M. Elsawah, 2015. Asymptotic distributions of the generalized range, midrange, extremal quotient, and extremal product, with a comparison study. Commun. Stat. - Theory Methods, 44: 900-913.
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  35. Elsawah, A.M. and H. Qin, 2014. New lower bound for centered L2-discrepancy of four-level U-type designs. Stat. Probab. Lett., 93: 65-71.
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  36. Elsawah, A.M. and H. Qin, 2014. Lee discrepancy on symmetric three-level combined designs. Stat. Probab. Lett., 96: 273-280.
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