The Exponential T-X Gompertz Model for Modeling Real Lifetime Data: Properties and Estimation

Abstract

In the real world, many applications require enhanced variants of well-known distributions. The new distributions are generally more adaptable for simulating real-world data with high skewness and kurtosis. Choosing the best statistical distribution for modeling data is very important and demanding. In this paper, we provide a new fl exible model for modeling lifetime data that is achieved by adding a component to baseline distributions. The new model has three parameters, known as the exponential T-X Gompertz distribution. Its probability density function could be skewed and unimodal. Reliability, hazard rate, quantile, and the moment generating function are just a few of the distributional properties that can be inferred from the suggested model. To estimate the unknown parameters, maximum likelihood estimation is utilized. In addition, Monte Carlo simulation experiments are performed to evaluate the performance of the maximum likelihood estimators. Finally, two real-world data sets are shown to evaluate the proposed model’s potential with that of various existing models.