在数据不确定的情况下分析希腊-拉丁方形设计

IF 8.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Journal of Big Data Pub Date : 2024-08-07 DOI:10.1186/s40537-024-00970-1

Abdulrahman AlAita, Muhammad Aslam, Khaled Al Sultan, Muhammad Saleem

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引用次数: 0

摘要

本文探讨了中性统计下的格拉诺-拉丁方阵设计（GLSD）。方法该方法包括确定中性方差分析以及确定中性假设和决策规则。结果利用数值示例和模拟研究评估了拟议设计的性能。结论根据观察到的结果，可以得出结论：在存在不确定性的情况下，中性统计下的 GLSD 比经典统计下的 GLSD 性能更好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Analysis of Graeco-Latin square designs in the presence of uncertain data

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Analysis of Graeco-Latin square designs in the presence of uncertain data

Objective

This paper addresses the Graeco-Latin square design (GLSD) under neutrosophic statistics. In this work, we propose a novel approach for analyzing Graeco-Latin square designs using uncertain observations.

Method

This approach involves the determination of a neutrosophic ANOVA and the determination of the neutrosophic hypotheses and decision rule.

Results

The performance of the proposed design is evaluated using the numerical examples and simulation study.

Conclusion

Based on the results observed, it can be concluded that the GLSD under neutrosophic statistics performs better than the GLSD under classical statistics in the presence of uncertainty.

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来源期刊

Journal of Big Data Computer Science-Information Systems

CiteScore

17.80

自引率

3.70%

发文量

105

审稿时长

13 weeks

期刊介绍： The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.