Theory and Practice on Non-Probabilistic Data and Analysis: a bibliometric review

• Autorzy: Sartori Jeanfrank Teodoro Dantas

Identyfikatory

DOI

10.2478/fcds-2024-0010

• Języki publikacji: EN

Abstrakty

EN

This bibliometric study aims to summarize the academic landscape of non-probabilistic data research, based on an examination of scientific output indexed in Web of Science and Scopus databases. It employs multiple methods to analyse and describe the collected corpus, including co-authorship and keyword co-occurrence networks to investigate patterns of collaboration and predominant research themes. Co-authorship analysis identified several robust research clusters, while keyword later spotlighted key thematic areas in the field. Countries, types of documents, categories, year of publication, citations and other metrics were also produced, and implications discussed. The findings present a structured overview of the non-probabilistic data research landscape, delineating the research trends, prominent authors, and emerging themes.

Słowa kluczowe

EN

non-probabilistic; non-random; statistics; bibliometric; review

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2024
• Tom: Vol. 49, No. 2
• Strony: 161--180
• Opis fizyczny: Bibliogr. 48 poz., rys., tab.

Twórcy

autor

Sartori Jeanfrank Teodoro Dantas

• Business School Positivo University, Curitiba, Brazil

Bibliografia

• [1] Aven, T. (2013). On how to define, understand and describe risk. Reliability Engineering & System Safety, 144, 1-10.
• [2] Aven, T. (2016). Risk assessment and risk management: Review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1-13.
• [3] Baltar, F., & Brunet, I. (2012). Social research 2.0: virtual snowball sampling method using Facebook. Internet Research.
• [4] Battaglia, A. G. (2008). Non-probabilistic treatment of uncertainty in engineering design: Literature review and application to composites. Composites Part B: Engineering, 39(5), 750-756.
• [5] Beer, M., Ferson, S., & Kreinovich, V. (2013). Imprecise probabilities in engineering analyses. Mechanical Systems and Signal Processing.
• [6] Ben-Haim, Y. (1994). A nonprobabilistic concept of reliability. Structural Safety.
• [7] Borgatti, S. P. (2005). Centrality and network flow. Social networks, 27(1), 55-71.
• [8] Certo, S. T., Busenbark, J. R., & Woo, H.-S. (2016). Sample selection bias and Heckman models in strategic management research. Strategic Management Journal.
• [9] Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382-1402.
• [10] Deutsch, D. (1999). Quantum theory of probability and decisions. Proceedings of the Royal Society A-Mathematical Physical and Engineering Sciences, 455(1988), 3129-3137.
• [11] Dubois, D., & Prade, H. (1988). Possibility theory: An approach to computerized processing of uncertainty. Plenum Press.
• [12] Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact?. Scientometrics, 105(3), 1809-1831.
• [13] Fang, F., Zhang, H., Cheng, J., Roy, S., & Leung, V. C. M. (2017). Joint User Scheduling and Power Allocation Optimization for Energy-Efficient NOMA Systems With Imperfect CSI. IEEE Journal on Selected Areas in Communications, 35(12), 2874-2885.
• [14] Fullerton, R. R., & Wempe, W. F. (2009). Lean manufacturing, non-financial performance measures, and financial performance. International Journal of Operations & Production Management.
• [15] Jiang, C., Han, X., Lu, G. Y., Liu, J., Zhang, Z., & Bai, Y. C. (2011). Correlation analysis of non-probabilistic convex model and corresponding structural reliability technique. Computer Methods in Applied Mechanics and Engineering.
• [16] Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications. Prentice Hall.
• [17] Kosko, B. (1992). Neural networks and fuzzy systems: A dynamical systems approach to machine intelligence. Prentice Hall.
• [18] Liu, B., & Liu, Y. K. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on Fuzzy Systems, 10(4), 445-450.
• [19] Liu, H. C., & Pedrycz, W. (2018). Evaluation of feature selection approaches in software defect prediction. Information Sciences, 451-452, 135-147.
• [20] Liu, H. C., You, J. X., You, X. Y., & Shan, M. M. (2015). A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method. Applied Soft Computing, 28, 579-588.
• [21] Loos, R., Locoro, G., Comero, S., Contini, S., Schwesig, D., Werres, F., Balsaa, P., Gans, O., Weiss, S., Blaha, L., Bolchi, M., & Gawlik, B. M. (2010). Pan-European survey on the occurrence of selected polar organic persistent pollutants in ground water. Water Research.
• [22] Luo, Y., Kang, Z., Luo, Z., & Li, A. (2009). Continuum topology optimization with non-probabilistic reliability constraints based on multi-ellipsoid convex model. Structural and Multidisciplinary Optimization, 39(3), 273-290.
• [23] Moeller, B., & Beer, M. (2008). Engineering computation under uncertainty-capabilities of non-traditional models. Computers & Structures, 86(10), 1044-1061.
• [24] Moens, D., & Hanss, M. (2011). Non-probabilistic finite element analysis for parametric uncertainty treatment in applied mechanics: Recent advances. Finite Elements in Analysis and Design, 47(1), 4-16.
• [25] Moens, D., & Vandepitte, D. (2005). A survey of non-probabilistic uncertainty treatment in finite element analysis. Computer Methods in Applied Mechanics and Engineering.
• [26] Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106(1), 213-228.
• [27] Newman, M. E. (2001). The structure of scientific collaboration networks. Proceedings of the national academy of sciences, 98(2), 404-409.
• [28] Qiu, Z. P., & Elishakoff, I. (1998). Antioptimization of structures with large uncertain-but-non-random parameters via interval analysis. Computer Methods in Applied Mechanics and Engineering.
• [29] Qiu, Z. P., & Wang, X. J. (2003). Comparison of dynamic response of structures with uncertain-but-bounded parameters using non-probabilistic interval analysis method and probabilistic approach. International Journal.
• [30] Reiners, C., Wegscheider, K., Schicha, H., Theissen, P., Vaupel, R., Wrbitzky, R., & Schumm-Draeger, P. M. (2004). Prevalence of thyroid disorders in the working population of Germany: Ultrasonography screening in 96,278 unselected employees. Thyroid.
• [31] Sartori, J. T. D. (2023). True Confidence Level of Real-World Data: implications of non-probabilistic data on decision-making and research. Italy, 48p.
• [32] Shafer, G. (1976). A mathematical theory of evidence (Vol. 1). Princeton university press.
• [33] Silver Ian, A. & Kelsay James, D. (2023) The moderating effects of population characteristics: a potential biasing factor when employing non-random samples to conduct experimental research. Journal of Experimental Criminology, 19(1), 107-118.
• [34] Simoen, E., De Roeck, G., & Lombaert, G. (2015). Dealing with uncertainty in model updating for damage assessment: A review. Mechanical Systems and Signal Processing.
• [35] Szmidt, E., & Kacprzyk, J. (2001). Entropy for intuitionistic fuzzy sets. Fuzzy Sets and Systems.
• [36] Tian, W., Heo, Y., de Wilde, P., Li, Z., Yan, D., Park, C. S., Feng, X., & Augenbroe, G. (2018). A review of uncertainty analysis in building energy assessment. Renewable & Sustainable Energy Reviews.
• [37] Trochim, W. M. (2006). The research methods knowledge base (Vol. 2). Atomic Dog.
• [38] Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.
• [39] Vasconcelos Sampaio, P. G., & Aguirre Gonzalez, M. O. (2017). Photovoltaic solar energy: Conceptual framework. Renewable & Sustainable Energy Reviews.
• [40] Wang, Y. M., & Guan, Z. L. (2018). Environmental performance evaluation using a non-radial DEA model: In case of lake water quality in China. Journal of Cleaner Production, 176, 63-74.
• [41] Yi-Chuan, F., Yong-Juan, W., Jin-Long, S. & Tong-Guang, G. (2023) Reliability analysis of mechanisms with mixed uncertainties using polynomial chaos expansion. Quality and Reliability Engineering International, 39(4), 1248-1268.
• [42] Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and Systems, 1(1), 3-28.
• [43] Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4), 83-93.
• [44] Zadeh, L. A. (1996). Fuzzy sets. In Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems: Selected Papers by Lotfi A Zadeh (pp. 394-432). World Scientific.
• [45] Zeng, M., Changquan, L. & Peng, H. (2023) Unified reliability-based design optimization with probabilistic - uncertain-but-bounded and fuzzy variables. Computer Methods in Applied Mechanics and Engineering, 407.
• [46] Zhang, D., & Jiang, B. (2019). Bibliometric and visualized analysis of emergy research. Ecological Modelling, 392, 78-88.
• [47] Zhaoping, T., Jun, P., Jianping, S. & Xin, M. (2022) Non-Probabilistic Reliability Analysis of Robot Accuracy under Uncertain Joint Clearance. Machines, 10(10).
• [48] Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472.