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A Neutrosophic Approach to the Minimum Variance Bound: Theory, Simulation, and Application

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  • Muhammad Aslam

    (King Abdulaziz University)

Abstract

The minimum variance bound (MVB) is a key concept used to determine the minimum achievable variance for an estimator. However, a limitation of the traditional MVB under classical statistics is its applicability only to precise data. In reality, statistical data often contain imprecision, making the classical MVB less effective. This paper extends the MVB by incorporating neutrosophic statistics, which can account for uncertainty or indeterminacy in the data. We introduce the theory of the neutrosophic minimum variance bound (NMVB) and simulate its application using both normal and Poisson distributions. The simulation results demonstrate a significant difference between the MVB and NMVB. Additionally, we apply the NMVB to real-world data and observe that the MVB is sensitive to changes in sample size and the degree of indeterminacy.

Suggested Citation

  • Muhammad Aslam, 2025. "A Neutrosophic Approach to the Minimum Variance Bound: Theory, Simulation, and Application," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 87(2), pages 595-615, August.
    • Handle: RePEc:spr:sankha:v:87:y:2025:i:2:d:10.1007_s13171-025-00383-z
    DOI: 10.1007/s13171-025-00383-z
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