A bimodal model for extremes data.

In extreme values theory, for a sufficiently large block size, the maxima distribution is approximated by the generalized extreme value (GEV) distribution. The GEV distribution is a family of continuous probability distributions, which has wide applicability in several areas, including hydrology, engineering, science, ecology, and finance. However, the GEV distribution is not suitable for modeling extreme bimodal data. In this paper, we propose an extension of the GEV distribution that incorporates an additional parameter. The additional parameter introduces bimodality and aries tail weight, i.e., this proposed extension is more flexible than the GEV distribution. Inference for the proposed distribution was performed under the likelihood paradigm. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. Finally, the proposed distribution is applied to environmental data sets, illustrating their capabilities in challenging cases in extreme value theory. [ABSTRACT FROM AUTHOR]