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: Appendix to the Critical Review of Stochastic Simulation Literature and Applications for Health Actuaries. The Development of a Simulation Model of Primary Prevention Strategies for Coronary Heart Disease

Description: One of the first projects undertaken by Claim Analytics was to create a model to predict return to work for group insurance claimants with long term disabilities. This report, sponsored in part by the Society of Actuaries Health Section , is a case study on creating such a model.

Description: A study on stochastic simulation literature as it applies to a health actuary.

Description: This report contains estimates of incidence and continuance rates from the general population using data available from the National Long–Term Care Surveys. This report was done by a subcommittee of the Society's Long–Term Care Experience Committee and reflects work done primarily at the Duke University Center for Demographic Studies. This is a ZIP file containing main report, all appendices, tables, and figures from the study and model.

Description: An introduction to Hidden Markov Models, which are used extensively in speech and facial recognition technology. They are a useful tool in forecasting time series and in developing categorizations of data based on the values of the data themselves.

Description: This paper proposes a more generalized Gerber-Shiu G-S function which contains both the G_S expected discounted penalty function and dividends paid up to ruin, as well as many others that have not been taken into consideration in the same systematic way, such as the insurer's accumulative utility, expected total discounted claim expenses, etc. The major result provides a unifying approach for all ruin-related quantities that fall in the category of the generalized GS function. From the Actuarial Research Clearing House, 2008, Vol. 1.

Description: A study of disability experience examines cost burden and degree of disability, and how these measures vary across different subpopulations. This article summarizes a presentation given at the Society of Actuaries’ Living to 100: Survival to Advanced Ages International Symposium held on Jan. 7–9, 2008 in Orlando, Florida. An abstract of the author's paper was presented in the September 2008 issue of Long-Term Care News.

Description: This paper endeavors to obtain the explicit formulas of the probability of ruin in a Markov-modulated model where claim intensities, claim sizes and premiums vary according to a Markovian environment.

Description: In this continuation of our July 2013 article we explore an algorithm for evaluating Hidden Markov Models in detail. A preferred method in speech recognition technology, Hidden Markov Models are a promising technique when dealing with data you suspect may be driven by a change in a unobservable state. The example in the article is provided in an Excel workbook for the reader to follow along with.