A Neutrosophic Extension of the Inverse Gamma Distribution: Properties and Applications

Abstract

This paper proposes a new the neutrosophic inverse gamma (NIG) distribution which is 
a generalization of the classical Inverse Gamma (IG) model to deal with vagueness, uncertainty and 
indeterminacy observed in healthcare and survival data. Since the traditional models are limited by 
rigid fixed parameters and are not capable of well approximating the fact that all imprecision arising 
from measurement errors, missing values, conflicting expert's assessments should be left in the 
imprecision of human judgement. The proposed model is more flexible and realistic, since shape 
and scale parameters can vary in certain intervals, by using neutrosophic logic. Some certain 
characteristics of the NIG distribution, such as the probability density function (PDF), cumulative 
distribution function (CDF), and moments are obtained and studied. The estimation techniques are 
also constructed for neutrosophic data. With simulations, random samples were generated from the 
model and the model’s ability to capture uncertainty in the simulated data is illustrated. Simulated 
results indicate that NIG is providing more robust and informative parameter estimates as long as 
sample size increases. Eventually, solar sector industrial data is employed to validate the proposed 
methodology and highlights its effectiveness in decision making 

DOI: 10.5281/zenodo.16884248