Description: This presentation suggests a possible flexible solution to the time and resource problems of running a large number of stochastic interest rate scenarios, by selecting a representative subset. Each interest rate scenario consists of 30 future spot yield curves, where a reasonable number of points are specified on each curve such as 12. The distribution of the scenarios is approximated by the subset and each scenario in the subset has equal weight. The method is independent of the inter rate generator used.

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: There are three commonly used methods for compiling exposure and death data for use in obtaining crude mortality rates. They are lives, policies and amounts of insurance. This paper investigates the properties of estimators based on lives, amounts and robust alternatives. It is concluded that lives provide the best estimator, even when there is a moderate dependence of mortality rates on amounts of insurance. Presented at the Fourteenth Actuarial Research Conference, lowa City, lowa, September 6, 1979.

Description: In this paper,we present a method which first converges a two dimensional data to a univariate one and then uses the interval censoring technique to estimate the probability of failure of a Joint-life status from a heavily censored data. Our study shows that the life annuities of joint-life status calculated assuming independence overestimates the ones evaluated from an actual sample of population.

Description: This paper presents a random selection method with the Monte Carlo simulation technique in the estimation of loss reserves. The future loss development factors are randomly selected from a weighted empirical distribution of observed loss development factors. This nonparametric approach provides an estimate of the distribution of total loss reserve.

Description: This paper develops a method for estimating the future claim costs of known ongoing large medical claims. It applies Least Squares analysis of historical experience to derive two arrays of factors. One array is to be applied as a vector inner product with claim costs already incurred and/or paid to project a total future claim cost. The second array distributes the total future claim cost over the months following the analysis date.

Description: Article 1979 Research Conference by Klugman in The Actuary, December 1979, Volume 13, Number 10

Description: By the use of resampling and extreme value statistics we will develop a method to reduce the time and costs of testing insurance company insolvency. Most ruin models require assumptions about the surplus distribution and/or assumptions about the claims count and severity distributions. The actuary may not be comfortable in making these ruin assumptions. Instead, he or she may generate such distributions by running extensive computer simulations of corporate models, I introduce a method to reduce the number of simulations necessary' to estimate the 1 and 0.1 percentiles of the surplus distribution. This approach 'cuts off' the tail at approximately the tenth percentile of the sample distribution and generates new tails for the distribution by resampling. Averages of the new order statistics approximate the I and 0.1 percentiles.

Description: We apply decision theory to develop a credibility formula that minimizes a loss function that is a linear combination of a squared-error term and a second-derivative term. The squared-error term measures the accuracy of the estimator, while the second-derivative term constrains the estimator to be close to linear.

Description: This is a presentation From 38th Annual Actuarial Research Conference 8/9/2003, University of Michigan, Ann Arbor, Michigan. Pricing health insurance presents numerous statistical problems. This paper develops a non-parametric method for handling these problems. This method involves kernel density estimation of the charge level distribution and also kernel smoothing between the reimbursement function derivatives. It derives some properties of the estimates so obtained. It illustrates the method with some dental data and compares the results to some other simpler estimation techniques.