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Maximization of Mathai's Entropy under the Constraints of Generalized Gini and Gini mean difference indices and its Applications in Insurance

Statistical Physics, Diffusion Entropy Analysis and Information Theory commonly use Mathai's entropy which measures the randomness of probability laws, whereas welfare economics and the Social Sciences commonly use Gini index which measures the evenness of probability laws. Motivated by the principle of maximal entropy, we explore the maximization of Mathai's entropy subject to the conditions in the following scenarios: (i) the conditions of a density function and fixed mean; (ii) the conditions of a density function and fixed Generalized Gini index. We also maximizes the Mathai's entropy subject to the constraints of a given Gini mean difference index and the conditions of a density function. The obtained maximum entropy distribution is fitted to the loss ratios (yearly data) for earthquake insurance in California from 1971 through 1994 and its performance with some one-parameter distributions are compared.