Description: This paper presents an approach to analyzing continuing care retirement community [CCRC] data, and demonstrates the methods using data from a large CCRC. It is assumed that residents make transitions among a number of states that represent the levels of care required by residents. There is randomness associated with both the transition times and the states entered at these times. The model is conveniently characterized in terms of transition intensity functions, which represent the instantaneous rates of transition between pairs of states.

Description: Recurrence relations arise naturally in life contingencies for 2 reasons - the aggregate law of mortality, which relates the distribution of the future life times of 2 insureds, if one of them and the other is that most life insurance products can be constructed as a portfolio of 2 basic product types. We use the above simple idea and the extra freedom provided by the fractional age distribution to reveal a structure behind some common recursions. The structure has obvious pedagogical value. More interestingly, a software based on it has been implemented which facilitates actuarial computing.

Description: The conventional way to analyze grouped observations on a single variable is to construct histograms or, sometimes, fit a normal distribution. We extend this latter approach by allowing for transformations to near normality.

Description: This paper shows how discrete multivariate analysis or multidimensional contingency table methods may be applied to data arising in actuarial work. A brief introduction to the theory is followed by two detailed examples of its application. Finally, the concluding remarks indicate how the procedure could be incorporated into a complete rate-making procedure. This work was performed for the U.S. Department of Housing and Urban Development.

Description: Project portfolio risk management and risk analysis form one of the critical components of enterprise risk management. Organizations measure and analyze risks associated with projects, project portfolios, and programs. The important step of the process is quantitative risk analysis of project schedules using event chain methodology (ECM). ECM is a stochastic modeling technique for schedule risk analysis. All risks, including schedule and nonschedule-related risks, are assigned to a particular project and within this project to the particular activity or resource. Further, ECM allows one to model the relationship between project risks by defining risks that cause or trigger other risks. All risks and relationships between them will be presented on the project or portfolio Gantt charts using event chain diagrams. After risks are assigned to project and portfolio schedules, Monte Carlo simulation of the project schedule is performed. ECM simplifies complex risk analysis process.