Description: In a product warranty, the renewal function is the expected number of failures in a specific warranty duration. To estimate the renewal function, cross-sectional regression data is assumed to be available and the data is assumed to be semiparametric. In this article, the observations are a11owed to be residuals from a complex regression model. It is observed that estimation can be accomplished using reasonable models for the contracts and data collection. From Actuarial Research Clearing House 1992, VOL. 2.

Description: Presented at October 1986 Annual Meeting. This session discussion is about the use of simulation, or Monte Carlo, techniques to analyze complex insurance models such as estimating reserves, solvency considerations, and estimation of mortality and distribution of aggregate losses with the aid of a computer program

Description: In this paper we use expectations of different random variables to define the notions of retrospective, prospective, individual, and aggregate net premiums for plans with contingent obligations.

Description: This is the abstract for the research paper on a hierarchial model for micro-level stochastic loss.

Description: This is the abstract for the paper on dependent multi-peril ratemaking models.

Description: This abstract describes a paper that introduces KDBINTMTM (Knowing, Doing, Being, Inspiring, and Networking), the framework in how it is being defined, and a plan to measure success for actuarial programs.

Description: The University of Wisconsin–Madison used its Centers of Actuarial Excellence grant to develop technology enhanced learning (TEL) tools that would be distributed publicly to supplement the actuarial education classroom experience.

Description: This abstract describes an article that outlines a process for creating an online actuarial text.

Description: The insurance industry is known to have high operating expenses in the financial services sector. Insurers, investors and regulators are interested in models to understand the behavior of expenses. However, the current practice ignores skewness, occasional negative values as well as their temporal dependence. Addressing these three features, this presentation develops a longitudinal model of insurance company expenses that can be used for prediction, to identify unusual behavior, and to measure firm efficiency. Specifically, it uses a three parameter asymmetric Laplace density for the marginal distribution of insurers’ expenses in each year. Copula functions are employed to accommodate their temporal dependence. As a function of explanatory variables, the location parameter allows the presenters to analyze an insurer’s expenses in light of the firm’s characteristics. Their model can be interpreted as a longitudinal quantile regression.

Description: Abstract of a presentation from Actuarial Research Clearing House 2012