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ANALYSIS OF CORONA PATIENTS USING UNCERTAINTY-BASED NON-PARAMETRIC MEDIAN TEST
Keywords:
medians, neutrosophy, Duckworth’s test, classical statistics, simulation.DOI:
https://doi.org/10.17654/0973514323018Abstract
Duckworth’s test is a well-known non-parametric statistical test used for comparing the medians of two populations. However, the conventional Duckworth’s test, based on classical statistics, is inadequate when dealing with data originating from neutrosophic populations. This paper presents a modified version of Duckworth’s test, specifically designed for neutrosophic statistics. This novel approach enables the application of Duckworth’s test to imprecise, uncertain, or data recorded in indeterminate intervals. The proposed test statistic under neutrosophic statistics is introduced and applied to real-world Covid-19 data. Through comprehensive analysis and simulation studies, the efficacy of the proposed Duckworth’s test under neutrosophic statistics is demonstrated to surpass that of the existing Duckworth’s test under classical statistics.
Received: August 7, 2023
Accepted: September 25, 2023
References
M. Abdel-Basset, A. Atef and F. Smarandache, A hybrid neutrosophic multiple criteria group decision making approach for project selection, Cognitive Systems Research 57 (2019), 216-227.
M. Abdel-Basset, M. Mohamed, M. Elhoseny, F. Chiclana and A. E.-N. H. Zaied, Cosine similarity measures of bipolar neutrosophic set for diagnosis of bipolar disorder diseases, Artificial Intelligence in Medicine 101 (2019), 101735.
M. Abdel-Basset, N. A. Nabeeh, H. A. El-Ghareeb and A. Aboelfetouh, Utilising neutrosophic theory to solve transition difficulties of IoT-based enterprises, Enterprise Information Systems 14(9-10) (2020), 1304-1324.
M. Aslam, Introducing Kolmogorov-Smirnov tests under uncertainty: an application to radioactive data, ACS Omega 5(1) (2019), 914-917.
M. Aslam, Neutrosophic analysis of variance: application to university students, Complex and Intelligent Systems 5(4) (2019), 403-407.
M. Aslam, Design of the Bartlett and Hartley tests for homogeneity of variances under indeterminacy environment, Journal of Taibah University for Science 14(1) (2020), 6-10.
M. Aslam, Radar data analysis in the presence of uncertainty, European Journal of Remote Sensing 54(1) (2021), 140-144.
S. Broumi, A. Bakali, M. Talea, F. Smarandache and K. Ullah, Bipolar neutrosophic minimum spanning tree, Infinite Study 127 (2018).
S. Broumi and F. Smarandache, Correlation coefficient of interval neutrosophic set, Applied Mechanics and Materials 436 (2013), 511-517.
J. Chen, J. Ye and S. Du, Scale effect and anisotropy analyzed for neutrosophic numbers of rock joint roughness coefficient based on neutrosophic statistics, Symmetry 9(10) (2017), 208.
J. Chen, J. Ye, S. Du and R. Yong, Expressions of rock joint roughness coefficient using neutrosophic interval statistical numbers, Symmetry 9(7) (2017), 123.
J. de Winter and D. Dodou, Five-point Likert items: t test versus Mann-Whitney-Wilcoxon (Addendum added: October 2012), Practical Assessment, Research, and Evaluation 15(1) (2010), 11.
T. Denoux, M.-H. Masson and P.-A. Hebert, Nonparametric rank-based statistics and significance tests for fuzzy data, Fuzzy Sets and Systems 153(1) (2005), 1-28.
D. Dubois and H. Prade, Ranking fuzzy numbers in the setting of possibility theory, Information Sciences 30(3) (1983), 183-224.
D. Feng, G. Cortese and R. Baumgartner, A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size, Statistical Methods in Medical Research 26(6) (2017), 2603-2621.
Y. Fong and Y. Huang, Modified Wilcoxon-Mann-Whitney test and power against strong null, The American Statistician 73(1) (2019), 43-49.
P. Grzegorzewski, Statistical inference about the median from vague data, Control and Cybernetics 27 (1998), 447-464.
P. Grzegorzewski, k-sample median test for vague data, International Journal of Intelligent Systems 24(5) (2009), 529-539.
M. Happ, A. C. Bathke and E. Brunner, Optimal sample size planning for the Wilcoxon-Mann-Whitney test, Statistics in Medicine 38(3) (2019), 363-375.
G. E. Haynam and Z. Govindarajulu, Exact power of Mann-Whitney test for exponential and rectangular alternatives, The Annals of Mathematical Statistics 37(4) (1966), 945-953.
M. Hollander, D. A. Wolfe and E. Chicken, Nonparametric Statistical Methods, Vol. 751, John Wiley & Sons, 2013.
J. Kacprzyk, E. Szmidt, S. Zadrozny, K. T. Atanassov and M. Krawczak, Advances in fuzzy logic and technology 2017, Proceedings of: EUSFLAT-2017 -The 10th Conference of the European Society for Fuzzy Logic and Technology, September 11-15, 2017, Warsaw, Poland IWIFSGN’2017 - The Sixteenth International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets, September 13-15, 2017, Warsaw, Poland, Vol. 2, Springer, 2017.
C. Kahraman, C. E. Bozdag, D. Ruan and A. F. Ozok, Fuzzy sets approaches to statistical parametric and nonparametric tests, International Journal of Intelligent Systems 19(11) (2004), 1069-1087.
G. K. Kanji, 100 statistical tests, 100 Statistical Tests, 2006, pp. 1-256.
K. R. Mollan, I. M. Trumble, S. A. Reifeis, O. Ferrer, C. P. Bay, P. L. Baldoni and M. G. Hudgens, Exact power of the rank-sum test for a continuous variable, 2019, arXiv preprint arXiv:1901.04597.
N. A. Nabeeh, F. Smarandache, M. Abdel-Basset, H. A. El-Ghareeb and A. Aboelfetouh, An integrated neutrosophic-topsis approach and its application to personnel selection: a new trend in brain processing and analysis, IEEE Access 7 (2019), 29734-29744.
R. G. Newcombe, Confidence intervals for an effect size measure based on the Mann-Whitney statistic. Part 1: general issues and tail-area-based methods, Statistics in Medicine 25(4) (2006a), 543-557.
R. G. Newcombe, Confidence intervals for an effect size measure based on the Mann-Whitney statistic. Part 2: asymptotic methods and evaluation, Statistics in Medicine 25(4) (2006b), 559-573.
X. Peng and J. Dai, Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function, Neural Computing and Applications 29(10) (2018), 939-954.
M. P. Perme and D. Manevski, Confidence intervals for the Mann-Whitney test, Statistical Methods in Medical Research 28(12) (2019), 3755-3768.
G. Qin and L. Hotilovac, Comparison of non-parametric confidence intervals for the area under the ROC curve of a continuous-scale diagnostic test, Statistical Methods in Medical Research 17(2) (2008), 207-221.
A. Shahin, Y. Guo, K. Amin and A. A. Sharawi, A novel white blood cells segmentation algorithm based on adaptive neutrosophic similarity score, Health Information Science and Systems 6(1) (2018), 1.
D. Shin, Y. Yamamoto, M. P. Brady, S. Lee and J. A. Haynes, Modern data analytics approach to predict creep of high-temperature alloys, Acta Materialia 168 (2019), 321-330.
F. Smarandache, Neutrosophy, Neutrosophic Probability, Set, and Logic, ProQuest Information and Learning, Ann Arbor, Michigan, USA, 105, 1998, pp. 118 123.
F. Smarandache, Introduction to neutrosophic statistics, Infinite Study (2014).
doi:10.13140/2.1.2780.1289.
F. Smarandache, Introduction to Neutrosophic Statistics, Sitech and Education Publisher, Craiova, Romania-Educational Publisher, Columbus, Ohio, USA, 123, 2014.
F. Smarandache, NIDUS IDEARUM, Scilogs, V: joining the dots, Infinite Study 5 (2019).
S. Taheri and G. Hesamian, A generalization of the Wilcoxon signed-rank test and its applications, Statistical Papers 54(2) (2013), 457-470.
S. M. Taheri and M. Arefi, Testing fuzzy hypotheses based on fuzzy test statistic, Soft Computing 13(6) (2009), 617-625.
S. M. Taheri and G. Hesamian, Non-parametric statistical tests for fuzzy observations: fuzzy test statistic approach, International Journal of Fuzzy Logic and Intelligent Systems 17(3) (2017), 145-153.
K. Vermeulen, O. Thas and S. Vansteelandt, Increasing the power of the Mann-Whitney test in randomized experiments through flexible covariate adjustment, Statistics in Medicine 34(6) (2015), 1012-1030.
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