Wang X, Kang W
Volume6-Issue11
Dates: Received: 2025-10-09 | Accepted: 2025-11-03 | Published: 2025-11-05
Pages: 1626-1642
Abstract
This paper proposes a set of non-parametric statistical functions designed to analyse non-linear systems within the context of refined educational management. Recognizing the limitations of traditional parametric methods—particularly their reliance on stringent distributional assumptions this research develops a comprehensive framework that accommodates the complexities inherent in educational data, which often exhibit non-normal distributions and diverse variable types. The proposed functions enable robust evaluation of relationships among educational metrics, facilitating improved hypothesis testing and the identification of meaningful patterns in student performance and teaching effectiveness. Furthermore, the study discusses the practical application of these methods in educational settings, demonstrating their utility in evaluating program efficacy and informing data-driven decision-making processes. Through methodological innovation, this work aims to enhance analytical rigor in educational research, ultimately contributing to improved management strategies and optimized learning outcomes in dynamic educational environments. Future work will explore integration with federated learning for multi-institutional data collaboration.
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DOI: 10.37871/jbres2216
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© 2025 Kang W, et al. Distributed under Creative Commons CC-BY 4.0 
How to cite this article
Wang X, Kang W. A Novel Neural Network-Based Statistical Method. J Biomed Res Environ Sci. 2025 Nov 05; 6(11): 1626-1642. doi: 10.37871/jbres2216, Article ID: JBRES2216, Available at: https://www. jelsciences.com/articles/jbres2216.pdf
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References
- Ebm C, Del Pozo C, Barbarello A, Poli G, Brusa S. Unleashing excellence: using a project management approach to effectively implement a simulation curriculum to improve residents' preparedness. BMC Med Educ. 2024 Mar 4;24(1):234. doi: 10.1186/s12909-024-05166-y. PMID: 38438940; PMCID: PMC10913544.
- Fisher RA. On the interpretation of χ² from contingency tables, and the behavior of variates, and a new test for the significance of the difference between two percentages. Journal of the Royal Statistical Society. 1922;85(1):87-94.
- Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association. 1952;47(260):583-621.
- McNemar Q. Note on the use of the chi-square test for 2 × 2 tables. Psychological Bulletin. 1947;44(6):289-302.
- Panakaje N, Bhagwath AA, Riha Parvin SM, K M, Kambali U. Accelerating entrepreneurship: Evidence from the incubation centers of management institutes of Dakshina Kannada. Heliyon. 2024 Jul 9;10(14):e34312. doi: 10.1016/j.heliyon.2024.e34312. PMID: 39108867; PMCID: PMC11301167.
- Wachel P, Kowalczyk K, Rojas CR. Decentralized diffusion-based learning under non-parametric limited prior knowledge. European Journal of Control. 2024;75:100912.
- Sivabalan S, Minu RI. Optimized multi-anchor space-aware temporal convolutional neural network for automobile sales prediction. Knowledge-Based Systems. 2025;311:113000.
- Rao PS, Varma GP, Chinta DP. Financial time series prediction using pelican optimized extreme learning machine with reduced weights. Computational Economics. 2025.
- Nuñez I, Cano EE, Cruz E. Improving usability in mobile apps for residential energy management: A hybrid approach using fuzzy logic. Applied Sciences. 2024;14:1751.
- Xu X. AI optimization algorithms enhance higher education management and personalized teaching through empirical analysis. Sci Rep. 2025 Mar 24;15(1):10157. doi: 10.1038/s41598-025-94481-5. PMID: 40128297; PMCID: PMC11933401.
- Liu Q, An X, Chen W. College students' entrepreneurship education path and management strategy of start-up enterprises using causal attribution theory. Sci Rep. 2025 Jan 21;15(1):2706. doi: 10.1038/s41598-025-86797-z. PMID: 39837953; PMCID: PMC11751438.
- Zhao Y. World class learners: Educating creative and entrepreneurial students. Corwin Press; 2012.
- Saavedra AR, Engel LC. Learning 21st century skills: A global perspective. International Academy of Education. 2012.
- Orfield G, Lee C. Why segregation matters: Poverty and educational inequality. The Civil Rights Project, Harvard University; 2005.
- Fullan M. Change the system: Reforming education. Institute for Educational Leadership. 2007.
- Darling HL. Teacher education and the American future. Teachers College Record. 2010;112:2920-2953.
- National Science Foundation. Science and engineering indicators 2020. NSF. 2020.
- Ioannidis JPA. Why most published research findings are false. PLoS Medicine. 2005;2:e124.
- Facilitating interdisciplinary research. National Academy of Sciences. The National Academies Press; 2005.
- Dijk DV, Dijk DE. The impact of academic publication pressure. Research Policy. 2014;43:1366-1380.
- Ming Z, Huang Y, Hu C. AI-driven knowledge-data graph for tracking global arbovirus research. J Big Data. 2025;12.
- Zhang M. AI-Driven Statistical Modeling for Social Network Analysis. IEEE Access. 2024;12:152766-152776.
- Wilkinson S, Ellison SL, Bottrell C. The limitations (And potential) of non-parametric morphology statistics for post-merger identification. Monthly Notices of the Royal Astronomical Society. 2024;528:5558-5585.
- Cochran WG. The comparison of percentages in matched samples. Biometrika. 1950 Dec;37(3-4):256-66. PMID: 14801052.
- Kang W. A merged fuzzy system and neural network for improving management method and strategy in scientific research and education. Sci Rep. 2025 Jul 1;15(1):21491. doi: 10.1038/s41598-025-07564-8. PMID: 40596609; PMCID: PMC12218241.
- Christensen R. Multiple comparisons: Theory and methods. Technometrics. 1997;39:232-233.
- Mann HB, Whitney DR. On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics. 1947;18:50-60.
- Smirnov NV. On the estimation of the discrepancy between empirical distribution functions of two samples. Bulletin of Moscow University, Series A. 1948;2:3-15.
- WILCOXON F. Individual comparisons of grouped data by ranking methods. J Econ Entomol. 1946 Apr;39:269. doi: 10.1093/jee/39.2.269. PMID: 20983181.
- He H. A method for sharing English education resources in multiple virtual networks based on 6G. International Journal of Network Management. 2025;35:e2319.
- Kolmogorov AN. Sulla determinazione empirica di una legge di distribuzione. Giornale dell’Istituto Italiano degli Attuari. 1933;4:83-91.
- Al-Essa LA, Etman WBH, Eliwa MS, El-Morshedy M, El-Sagheer RM. A novel non-parametric statistical method in reliability theory: Mathematical characterization and analysis of asymmetric data in the fields of biological sciences and engineering. Heliyon. 2024 Jul 1;10(13):e33596. doi: 10.1016/j.heliyon.2024.e33596. PMID: 39670229; PMCID: PMC11637204.
- Lehmann EL. Introduction to student (1908) the probable error of a mean. In: Springer Series in Statistics. Springer, New York; 1992.
- Cuevas C, Cortés C, García N. Empowering computer vision in higher education: A novel tool for enhancing video coding comprehension. Computer Applications in Engineering Education. 2024;32:e22808.
- Chugh R, Turnbull D, Morshed A. The promise and pitfalls: A literature review of generative artificial intelligence as a learning assistant in ICT education. Computer Applications in Engineering Education. 2025;33:e70002.
