Analyzing insurance data with an exponentiated composite Inverse-Gamma Pareto model
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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 introduced 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 the well-known Danish fire insurance data and Norwegian fire insurance data. The two-parameter exponentiated Inverse-Gamma Pareto model outperforms the one-parameter Inverse-Gamma Pareto model in terms of goodness-of-fit measures for both datasets.
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