Volume 25 , Issue 4 , PP: 305-315, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmedia Musa M. Ibrahim 1 *
Doi: https://doi.org/10.54216/IJNS.250426
We provide a neutrosophic approach to the Pareto model, which is widely used to model survival data. In this paper, the neutrosophic Pareto model (NPM) is constructed under the framework of neutrosophic statistics, that can manage uncertain nature of data, commonly occur in many real word problems. This formulation generalizes the classical model and is a useful method for dealing with fuzzy or uncertain data typically encountered in many applications in survival data. Using neutrosophic statistical framework, few key mathematic qualities of the proposed model such as its moments, survival function, and hazard rate are presented in the study. These properties are motivated and rigorously established to ensure theoretical soundness of the proposed model. Moreover, the maximum likelihood estimation (MLE) is used to estimate the neutrosophic parameters of the distribution. This approach is essential for deriving accurate parameter estimates from the data available, especially in cases where uncertainty or imprecision is present within the data as it is usually the case for any real-world situation. Based on the simulation experiment, we display the adequate performance of the suggested model. The simulations allow us to evaluate the performance of the routine as well as the stability of the model parameters across different settings. At the end, the real data analysis is conducted to show the applicability of proposed approach. The proposed model processes such a dataset filled with a range of uncertain values and presents its possibilities to be applied for information extraction from real world data sets that are abundant in uncertainty. Our results open a new avenue for neutrosophic statistical model approaches to the analysis of survival data in subsequent studies.
Survival model , Estimation , Simulation , Neutrosophic logic , Neutrosophic distribution
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