Exponentiated Generalized Exponential Geometric Distribution: Model, Properties and Applications

Abstract

In this article, a new distribution called Exponentiated Generalized Exponential Geometric Distribution is formulated. We have derived some important mathematical properties like hazard function, probability density function, survival function, quantiles, the measures of skewness based on quartiles and coefficient of kurtosis based on octiles. To estimate the parameters of the new distribution, we have applied the three commonly used estimation methods namely maximum likelihood estimation (MLE), least-square (LSE) method and Cramer-Von-Mises (CVM) method. We have used R programming as well as analytical methods for data analysis. For model validation, we have used different information criteria as Akaike’s information criteria, and Bayesian information criteria (BIC) etc. For the assessment of potentiality of the new distribution, we have considered a real dataset and the goodness-of-fit attained by proposed distribution is compared with some competing distributions. It is found that the proposed model fits the data very well and more flexible as compared to some other models.