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: 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: 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.

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: This abstract describes a paper that describes various Bayesian methods for fitting regime-switching models and compares their benefits and disadvantages.

Description: This abstract describes a paper that investigates how to best choose a model from a flexible set of regime-switching models where the individual regimes are not constrained to be from the same distributional family.

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: 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.