Description: When fitting a model to any data, there is some uncertainty about which model is best. Green [1995] quantifies this uncertainty through the Reversible Jump Markov Chain Monte Carlo [RJMCMC] method. When using the RJMCMC method in a regime-switching situation, the chain determines the optimal number of regimes by jumping between various possibilities. This method gives each model its posterior probability of being the best. After an overview of the methodology, the authors apply it to various datasets and discuss the applications in modern actuarial science.

Description: This abstract describes a paper that, using data from a major insurer, compares and contrasts several different models for risk assessment, including GLMs, GLMMs, and GAMs.

Description: Techniques that have gained some traction in general (property/casualty) insurance will be highlighted with a particular focus on the linear mixed model. This model adds random effects to the more commonly seen fixed effects (for example, as used in GLMs).

Description: This study examines and describes parameter uncertainty. The paper provides structures to incorporate parameter uncertainty in quantifying risk.

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

Description: LTC insurers are beginning to implement population health programs to improve the lives of their policyholders before and during a claim. Measuring the financial impact of these programs can be difficult for a variety of reasons and multiple methodologies are used to evaluate treatments or programs. This article proposes two different methods of randomized control trials to evaluate the impact of these programs through a case study approach, along with additional potential considerations when evaluating effectiveness.

Description: This abstract describes a presentation in which the use of the propensity score in health care studies can minimize biases stemming from non-random treatment assignment and lead to appropriate conclusions on the effect of the intervention.

Description: The Society of Actuaries Smaller Insurance Company Section, the Financial Reporting Section, and the Committee on Life Insurance Research are pleased to make available new research investigating simplified methods for determining reserves under VM-20.