Abstracts and Reviews Ktinig A., Schradin H.R., Deutsche Gesellschafifiir Versicherungsmathematik, Bli~tter, Band XXII, Heft 3, April 1996, pp. 515-542 Since July 29, 1994, it is permitted to use different interest rates for premium calculation and determination of net premium reserves. Taking the perspective of a life insurance company, the subject of the article is to discuss the consequences for liquidity and yield resulting from these extended calculation possibilities. Moreover, the evolution of the assets generated by the life insurance contract is analysed in the context of a Monte Carlo simulation. Ultimately, the cumulative distribution function of the insurers' surplus is estimated and statistical figures are determined and discussed. Keywords: Liquidity, Yield, Monte Carlo. 082009 (M02, E61) Jahrbuch der Lebensversicherungen 1996, McKinsey
& Company, Sch~ffer-Poesschel-Verlag, 1996 In the present 17th edition of this yearbook, the annual statements of 78 German life assurance companies are analyzed in great detail. These companies represent 97,3 % (sum insured) or 97,9 % (premium income) of the German market, respectively. Information is given on - the development of the companies since 1988, - the development of business and market share, - the sources of surplus and the bonus reserve, - the structure of the portfolio and of the new business, - various cost elements, - development and structure of capital investment, and numerous further ratios. The analyses given provide a valuable impression of the situation of each company and an excellent survey of the German life assurance market. Keywords: Mortality Rates, Trends at oldest Ages.
M03: DATA BANKS 082010 (M03) Developing a Subject-Derived Terminology to Describe Perceptions of Chemicals in Foods. Raats M.M., Shepherd R., Risk Analysis, Vol 16, n °
2, 1996, pp. 133-146 Risk perception may be influenced by a number of factors, such as unfamiliarity, lack of control, perceived consequences, and hazards being seen as catastrophic and having risk for future generations. Risk
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perception researchers have typically used such investigator-selected characteristics to assess hazards. In the first study reported here, the repertory grid method was used to elicit the terminology that subjects (n = 30) use to distinguish between 30 different chemicals. The data were submitted to generalized Procrustes analysis. The first principal axis of the resulting consensus plot separated the chemicals ranging from "poisonous or toxic," "harmful or dangerous," and "sounds negative" at one end, to "positive effect on health," "often present in food nowadays,"and "sounds positive" at the other end. The second principal axis ranged from "familiar with or knowledge of' and "chemical" to "natural." A second study (n = 226) was carried out to look at the general validity of the results of the repertory grid interviews using a fixed questionnaire. The data were submitted to principal components analysis and internal preference mapping. The first principal component ranged from "safe" and "healthy" at one end, to "poisonous" and "harmful" at the other end. The chemicals also separated in terms of "familiar," "chemical," and "natural." All three methods of data collection and analysis yield essentially similar results. Keywords: Chemicals
M10: PROBABILITY THEORY AND
MATHEMATICAL STATISTICS IN INSURANCE, GENERAL AND MISCELLANEOUS 082011 (MI0) A Comparison of Two Statistical Approaches to Estimate Long-Term Exposure Distributions from Short-Term Measurements.
Slob W., RiskAnalysis, Vol. 16, n ° 2, 1996, pp. 195-200 Two statistical approaches are briefly reviewed, both of which are suitable for estimating interindividual variation in long-term exposure: a recently published regression approach and the standard ANOVA approach. Simulation studies illustrate the performances of the two approaches in estimating the relevant parameters. Their relative advantages and applicability are discussed. It is concluded that when repeated exposure measurements from the same individuals are available, ANOVA is preferable. The regression approach however has its place because it can be applied to certain data types where ANOVA does not apply. Keywords: Anova.