Random Numbers Generation and Goodness-of-Fit Testing for Literal Neutrosophic Numbers

Abstract

This paper presents an algorithm for generating random numbers that follow literal neutrosophic distributions using algebraic isomorphisms. The algorithm was applied to three distributions: literal neutrosophic uniform distribution, literal neutrosophic exponential distribution, and literal neutrosophic normal distribution. We also proposed a development of the Kolmogorov-Smirnov test to analyze goodness-of-fit for literal neutrosophic data. To evaluate the effectiveness of the proposed method, a simulation study was conducted, and the power results showed that increasing the sample size improved the test power. Our research contributes to the development of statistical methods for analyzing literal neutrosophic data, which has applications in a wide range of fields.