  Exponentiated models have been widely used in modeling various types of data
such as survival data and insurance claims data. However, the exponentiated
composite distribution models have not been explored yet. In this paper, we
introduce an improvement of the one-parameter Inverse Gamma-Pareto composite
model by exponentiating the random variable associated with the one-parameter
Inverse Gamma-Pareto composite distribution function. The goodness-of-fit of
the exponentiated Inverse Gamma-Pareto was assessed using three different
insurance data sets. The two-parameter exponentiated Inverse Gamma-Pareto model
outperforms the one-parameter Inverse Gamma-Pareto model in terms of
goodness-of-fit measures for all datasets. In addition, the proposed
exponentiated composite Inverse Gamma-Pareto model provides a very good fit
with some well-known insurance datasets.
