Generalized Dinesh–Umesh–Sanjay generalized exponential distribution with application to engineering data

Abstract

Several academics have expanded the generalized exponential distribution. The generalized Dinesh–Umesh–Sanjay generalized exponential (GDUS-GE) distribution with three parameters is introduced. The GDUS-GE distribution outperforms the moment exponential distribution in terms of fit. For numerous GDUS-GE distribution characteristics, exact formulations for ordinary moments, incomplete and conditional moments, the moment generating function, and information measures are discovered. The maximum likelihood approach was used to estimate model parameters. A simulated study was used to explore the estimators’ behavior. Two real-world datasets were used to assess the practical significance of the GDUS-GE distribution. In terms of performance, we demonstrate that the GDUS-GE distribution outperforms all other competing models.