Description: This paper addresses the fuzziness in regression models. The goal is to present a test procedure to explicitly examine whether an independent variable has a clear functional relationship with the dependent variable in a specific regression model, or whether their relationship is fuzzy. To this end, the authors interpret the spread of the regression coefficients as a statistic measuring the fuzziness of the relationship between the corresponding independent variable and the dependent variable. They then derive test distributions based on the null hypothesis that such spreads could have been obtained with data generated by a classical regression model with random errors. The analysis is presented in conceptual rather than technical terms.

Description: The study is based on a sample of actuarial reports attached to the form 550C3, Schedule B, which were sent to the Department of Labor during the period June 1976 to May 1977.

Description: This introduction is followed by an overview of RA, which includes such things as the role of RA in ERM, the conceptualization of RA, and the role of FL as it relates to RA. We turn then to an overview of the FL methodology used in this study. The next three chapters are devoted to FL methodology as it relates to RA issues, and includes a discussion of FL modification of the risk matrix, FL modification of the Analytic Hierarchy Process, and the role of fuzzy optimization as it relates to RA. We turn then to examples of RAA-FL in the areas of operational risk, hazard risk, financial risk and strategic risk. This is followed by closing comments and observations. The study concludes with an annotated bibliography.

Description: Genetic algorithms [GAs] and neural networks [NNs] are currently being used in actuarial and financial modeling. Nonetheless, while there is a general awareness of the nature of these techniques, there is often only vague familiarity with the details of how they are implemented. This paper is intended to help alleviate this situation. Its purpose is to present an overview of GAs and NNs, which includes an explanation of what they are, how they work, and an example of how they are implemented.

Description: This paper examines four and-a-half years of prescription records for an elderly population who have relatively comprehensive out-of-hospital drug coverage under the Pharmaceutical Assistance Contract for the Elderly program in Pennsylvania. The result is a discussion of the determination of non-parametric estimations of the distributions. From Actuarial Research Clearing House 1991, Vol 1.

Description: This research provides a literature review on the topic of post-retirement financial strategies from the perspective of an individual who is approaching retirement age. The body of knowledge surveyed is the post-retirement financial strategies literature that is related to the risks of longevity, inflation, and investment. The literature sources are both academic and practitioner-focused. The end product is a documentation of and commentary on the state-of-the-art in the post-retirement financial strategies area. The intended audience is retirement professionals, and others, who need to be well informed about the current state of post-retirement financial strategies, as reported in the literature.

Description: This paper uses a Bayesian approach to persistency to explore retirement cost projection variability. The analysis is divided into two parts. In the first part a subjective population projection model is developed, based on a beta-binomial distribution for the number of participants persisting through a given age. Probability statements regarding such items as the number of participants at a given age and the distribution of retirees are developed. The second part shows how the model might be used to generate confidence intervals for retirement cost projections. Both the single-entry-age case and the multiple-entry-age case are considered. From Transactions of Society of Actuaries 1979, Vol. 31.

Description: Recent articles addressed the subject of fuzzy regression [FR] and the term structure of interest rates [TSIR]. Their approach relied on possibilistic regression. Although possibilistic regression has been used in many applications, it has a number of limitations, not the least of which is its nebulous relation to the least-squares concept. As an alternative to possibilistic regression, this paper uses fuzzy least square regression to investigate the TSIR.

Description: Letter to the editor from Mr. Hilary L. Seal, Lausanne Federal Polytechnic School, appearing in the 1979 ARCH.

Description: A mathematical framework for benefits and choices must be created to model benefit selection. This paper creates such a framework by defining benefit plans as reimbursement functions. These are then used with a defined choice function to calculate the cost deviation due to selection. Finally, utility functions can be applied to this framework to predict choice.