Exponentiated Gompertz Exponential (Egoe) Distribution: Derivation, Properties and Applications

Abstract

In this paper, a new probability distribution called Exponentiated Gompertz Exponential distribution was introduced which can help researchers to model different types of data sets. In proposed distribution we introduce a new shape parameter to Gompertz Exponential distribution, varied its tail weight such that it enhances its flexibility and performance. Furthermore, the maximum likelihood method was used in estimating the model’s parameters. Simulation method was used to investigate the behaviours of the parameters of the proposed distribution; the results showed that the mean square error and standard error for the chosen parameter values decrease as the sample size increases. The proposed distribution was tested on real life data, the results showed that EGoE performed better than the existing distribution in the literature and a strong competitor to other distributions of the same class. The results also showed that the distribution can be used as an alternative model in modelling lifetime processes.

Keywords

• Exponentiated Gompertz Exponential distribution
• maximum likelihood
• means square error
• quantile function
• parameters

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