Inflated models are generally used whenever there is an excess number of frequencies at particular count. In this study, a three-inflated Poisson (ThIP) distribution is proposed by mixing the Poisson distribution and a distribution to a point mass at three. Some of its distribution properties and reliability characteristics are studied. A simulation study is carried out to see the performance of the MLEs. In India Covid-19 implications on mental health have been abysmal. Covid-19 related suicide data of India during lockdown to the first gradual relaxation of the terms of the total lockdown (unlocking 1.0) are used to examine the appropriateness of the proposed distribution. Likelihood ratio test is used for discriminating between Poisson and the proposed distribution.

ABSTRACT

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Actuarial risks can be analyzed using heavy-tailed distributions, which provide adequate risk assessment. Key risk indicators, such as value-at-risk, tailed-value-at-risk (conditional tail expectation), tailed-variance, tailed-mean-variance, and mean excess loss function, are commonly used to evaluate risk exposure levels. In this study, we analyze actuarial risks using these five indicators, calculated using four different estimation methods: maximum likelihood, ordinary least square, weighted least square, and Cramer-Von-Mises. To achieve our main goal, we introduce and study a new distribution. Monte Carlo simulations are used to assess the performance of all estimation methods. We provide two real-life datasets with two applications to compare the classical methods and demonstrate the importance of the proposed model, evaluated via the maximum likelihood method. Finally, we evaluate and analyze actuarial risks using the abovementioned methods and five actuarial indicators based on bimodal insurance claim payments data.