Research article Special Issues

Neutrosophic geometric distribution: Data generation under uncertainty and practical applications

  • Received: 26 February 2024 Revised: 17 April 2024 Accepted: 23 April 2024 Published: 10 May 2024
  • MSC : 62A86

  • This paper introduces the geometric distribution in the context of neutrosophic statistics. The research outlines the essential properties of this new distribution and introduces novel algorithms for generating imprecise geometric data. The study explores the practical applications of this distribution in the industry, highlighting differences in data generated under deterministic and indeterminate conditions using detailed tables, simulation studies, and real-world applications. The results indicate that the level of uncertainty has a substantial impact on data generation from the geometric distribution. These findings suggest updating classical statistical algorithms to better handle the generation of imprecise data. Therefore, decision-makers should exercise caution when using data from the geometric distribution in uncertain environments.

    Citation: Muhammad Aslam, Mohammed Albassam. Neutrosophic geometric distribution: Data generation under uncertainty and practical applications[J]. AIMS Mathematics, 2024, 9(6): 16436-16452. doi: 10.3934/math.2024796

    Related Papers:

  • This paper introduces the geometric distribution in the context of neutrosophic statistics. The research outlines the essential properties of this new distribution and introduces novel algorithms for generating imprecise geometric data. The study explores the practical applications of this distribution in the industry, highlighting differences in data generated under deterministic and indeterminate conditions using detailed tables, simulation studies, and real-world applications. The results indicate that the level of uncertainty has a substantial impact on data generation from the geometric distribution. These findings suggest updating classical statistical algorithms to better handle the generation of imprecise data. Therefore, decision-makers should exercise caution when using data from the geometric distribution in uncertain environments.



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