Extended generalized extreme value distribution with applications in environmental data

Abstract

In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory, which has wide applicability in several areas including hydrology, engineering, science, ecology and finance. In this paper, we propose three extensions of the GEV distribution that incorporate an additional parameter. These extensions are more flexible than the GEV distribution, i.e., the additional parameter introduces skewness and to vary tail weight. In these three cases, the GEV distribution is a particular case. The parameter estimation of these new distributions is done under the Bayesian paradigm, considering vague priors for the parameters. Simulation studies show the efficiency of the proposed models. Applications to river quotas and rainfall show that the generalizations can produce more efficient results than is the standard case with GEV distribution.