Research from Towson University Provides New Study Findings on Risk Management (Bayesian Inference for the Loss Models via Mixture Priors): Insurance – Risk Management

2023 SEP 19 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on risk management is the subject of a new report. According to news originating from Towson, Maryland, by NewsRx correspondents, research stated, “Constructing an accurate model for insurance losses is a challenging task.”

The news journalists obtained a quote from the research from Towson University: “Researchers have developed various methods to model insurance losses, such as composite models. Composite models combine two distributions: one for part of the data with small and high frequencies and the other for large values with low frequencies. The purpose of this article is to consider a mixture of prior distributions for exponential-Pareto and inverse-gamma-Pareto composite models. The general formulas for the posterior distribution and the Bayes estimator of the support parameter th are derived. It is shown that the posterior distribution is a mixture of individual posterior distributions. Analytic results and Bayesian inference based on the proposed mixture prior distribution approach are provided.”

According to the news reporters, the research concluded: “Simulation studies reveal that the Bayes estimator with a mixture distribution outperforms the Bayes estimator without a mixture distribution and the ML estimator regarding their accuracies. Based on the proposed method, the insurance losses from natural events, such as floods from 2000 to 2019 in the USA, are considered. As a measure of goodness-of-fit, the Bayes factor is used to choose the best-fitted model.”

For more information on this research see: Bayesian Inference for the Loss Models via Mixture Priors. Risks, 2023,11(9). (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks11090156.

Our news editors report that additional information may be obtained by contacting Min Deng, Department of Mathematics, Towson University, Towson, MD 21252, United States. Additional authors for this research include Mostafa S. Aminzadeh.

ORCID is an identifier for authors and includes bibliographic information. The following is ORCID information for the authors of this research: Min Deng (http://orcid.org/0000-0001-6076-7762), Mostafa S. Aminzadeh (http://orcid.org/0000-0002-4827-6351).

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