Bivariate Sin-Generalized Exponential Model with Applications on Medicine, Economic, and Environmental Data

This paper focuses on answering the question; How we can construct a bivariate model with fewer parameters that serve many disciplines in lifetime data? The answer to this question is introduced in this paper by proposing a new bivariate model that depends on constructing a SinTransformation of its marginals in the Farlie-Gumbel Morgenstern (FGM) Copula Model. This bivariate model is named by Morgenstern Sin Generalized Exponential (MSGE) Distribution. Without adding any extra parameter, MSGE provides a statistical model which gives better results in the goodness of fit to medical, economic, and environmental real lifetime data compared with other well-known models. The mathematical procedure of MSGE is carried out for statistical and reliability properties, estimation via 3 methods, simulation, and real practical applications in the three mentioned disciplines. The results illuminate the effectiveness of MSGE for the practitioner without overloading extra parameters.