Description: An excerpt from a presentation at the 2014 Living to 100 Symposium, this article describes an open-source model for use by actuaries attempting to estimate future health benefit costs, discusses its strengths and limitations, and projects that spending for those age over 65 will take more than $13 trillion by 2055, about 50 percent of total medical expenditures.

Description: Presenter offers ways in which actuaries might set credibility levels to be used by underwriters to set group health insurance expected benefits. From the Record of the Society of Actuaries, Vol. 24, No. 2.

Description: Panelists discuss the process and challenges presented by mergers or acquisitions. From the Record of the Society of Actuaries, Vol. 24, No. 2.

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: By necessity, attributions of financial processes require approximations. Tools used for approximations include Aumann-Shapley, time step, step-through, average step-through, partial derivative, and average partial derivative. Significantly non-linear paths can cause problems during attribution. Time (theta) and market factors must be handled concurrently. Typically contractual factors can be handled separately. Average Step-Through, Aumann-Shapley and Aumann-Shapley-3 points perform the best given the same time period. Attribution should be done with fairly small time steps as opposed to using a lot of points in the Aumann-Shapley calculation, but using Aumann-Shapley-3 points as a last step has some advantages.

Description: In this paper, we study a survival regression model known as Cox's proportional Hazards model. We assume that the data available are already grouped and develop a minimum distance estimation procedure that is also applicable when the data are also left or right censored. Asymptotic properties of the estimators are established and the numerical implementation of the method based on an iterated reweighted least squares algorithm is discussed.

Description: Users of statistical packages should be more aware of the influence that outlying data points can have on their statistical analyses. Robust procedures provide formal methods to spot these outliers and reduce their influence. While a few robust procedures are mentioned in this article, one is emphasized and it is motivated by maximum likelihood estimation to make it seem more natural. In this article, the author will try to provide some background, beginning with maximum likelihood estimation of one parameter, that will hopefully make robust estimation seem natural and rather easy.