A STATISTICAL FRAMEWORK FOR EXTREME VALUES: THE APPLICATION OF GOMPERTZ INVERSE PARETO DISTRIBUTION

In many real-life situations, the classical distributions do not provide adequate fit to some real data sets. Thus, researchers introduced many generators by introducing one or more parameters to generate new distributions. The new generated distributions are more flexible as compare to the classical distributions. Some well-known generators are Marshal-Olkin generated family (MO-G) (Marshall and Olkin, 1997), the Beta-G by Eugene et al. (2002) and Jones (2004), Kumaraswamy-G (Kw-G for short) by Cordeiro and de Castro (2011) and McDonald-G (Mc-G) by Alexander et al. (2012), gamma-G (type 1) by Zografos and Balakrishnan (2009), gamma-G (type 2) by Risti´c and Balakrishnan (2012), gamma-G (type 3) by Torabi and Hedesh (2012) and log gamma-G by Amini et al. (2012), Exponentiated generalized-G by Cordeiro et al. (2011), Transformed-Transformer (T-X) by Alzaatreh et al. (2013) and Exponentiated (T-X) by Alzaghal et al. (2013), Weibull-G by Bourguignon et al. (2014) and Exponentiated half logistic generated family by Cordeiro et al. (2014).Ghosh et al. (2016) introduced a new generator of continuous distributions with two extra parameters called the Gompertz-G generator and studied some general mathematical properties of it.