Neutrosophic Design of the Exponential Model with Applications

Abstract

Operations on neutrosophic numbers generalize operations on crisp numbers. In this way, the neutrosophic approach quantifies data ambiguity and enables the generalization of the existing statistical model. This study presents an extension of the conventional exponential distribution in a neutrosophic context. Neutrosophic generalization is restricted to characterize the properties of the neutrosophic exponential distribution (NED); however, related results can
to other stochastic models for handling the situations involving uncertainties or vagueness in processing data. All essential features of the proposed NED, such as neutrosophic moments, neutrosophic distribution function, and other related quantities, are explored. The mathematical results in this work lay the groundwork for using the exponential distribution to produce drivers for other generalized models. The neutrosophic logic of the proposed model is illustrated with examples. The estimation technique for treating the imprecision in the unknown parameter is established. The performance of the estimator neutrosophic estimator has been evaluated through Monte Carlo simulation. Simulation findings reveal that a larger sample size provides reliable
estimation results.