Description: Come discuss the benefits and challenges of collaboration between academic institutions and industry stakeholders in shaping actuarial education and research agendas. Register for this event now.

Description: Poisson regression models are commonly used in actuarial science to model count data e.g., the number of claims and covariates. The standard parametric formulation assumes that the response to covariates is constant across the population under study. Additionally, the shape of the posterior distribution is rather inflexible. In practice, however, the response to covariates may depend on a latent class and the posterior distribution may be highly nonparametric. This paper proposes a Bayesian nonparametric model in which a Dirichlet process prior on the regression coefficients leads to a clustering of observations into groups having the same response to covariates. This paper illustrates the utility of this approach with an example dataset of diabetes deaths.

Description: This abstract describes a presentation that discusses models to predict damage from future storms and help the utility properly manage their risk.

Description: Using the Health Care Cost Institute (HCCI) database, we examined which characteristics best predict and describe high-cost members.

Description: Chronic Kidney Disease (CKD) affects many lives and has a large impact on health systems around the world. To better understand and predict costs for insurance plan members with CKD in the United States, we built a new model of their individual costs. Our model is the first to explicitly model both the CKD stage transition process and the distribution of costs given those stages.

Description: Using the Health Care Cost Institute (HCCI) database, we examined which characteristics best predict and describe high-cost members.