18 résultats
pour « bayesian »
This paper focuses on the development of #bayesian classification and regression tree (#cart) models for claims frequency modeling in non-life #insurance pricing. The authors propose the use of the zero-inflated #poisson distribution to address the issue of imbalanced claims data and introduce a general MCMC algorithm for posterior tree exploration. Additionally, the deviance information criterion (DIC) is used for model selection. The paper discusses the applicability of these models through simulations and real insurance data.
"We address the problem of sharing risk among agents with preferences modelled by a general class of comonotonic additive and law-based functionals that need not be either monotone or convex. Such functionals are called distortion riskmetrics, which include many statistical measures of risk and variability used in portfolio optimization and insurance."
"Bayesian estimates from experimental data can be influenced by highly diffuse or "uninformative" priors. This paper discusses how practitioners can use their own expertise to critique and select a prior that (i) incorporates our knowledge as experts in the field, and (ii) achieves favorable sampling properties. I demonstrate these techniques using data from eleven experiments of decision-making under risk, and discuss some implications of the findings."
"An efficient Bayesian Markov Chain Monte-Carlo method is developed to estimate the unknown parameters to address the computational complexity. Our empirical application to the mortality data collected for the Group of Seven (G7) nations demonstrates the efficacy of our approach."
"The primary contribution of this paper is to present a unified Bayesian CAT bond pricing framework based on uncertainty quantification of catastrophes and interest rates. Our framework allows for complex beliefs about catastrophe risks to capture the distinct and common patterns in catastrophe occurrences, and when combined with stochastic interest rates, yields a unified asset pricing approach with informative expected risk premia."
"Estimations of model parameters are presented under Bayesian framework using a combination of Gibbs sampler and Metropolis-Hastings algorithm. Predictions and applications of the proposed model in enterprise risk management and cyber insurance rate filing are discussed."
"This work presents a Bayesian-based semi-mechanistic model for a short-term forecast of pandemic risk."
"The model is a comprehensive template for assessing loss and subsequently the insurance for activities in the Arctic and sub-Arctic regions. Governmental and non-government organisations alike will benefit from the tool by using it as a loss estimation mechanism for liability for ship-source oil spills."