Description: This paper presents a method involving re-sampling 'with replacement but without random numbers', numerical convolutions for sums and quotients, and estimation of confidence intervals for trend in average size claim. In this paper, this method is applied to major medical claims.

Description: When fitting a model to any data, there is some uncertainty about which model is best. Green [1995] quantifies this uncertainty through the Reversible Jump Markov Chain Monte Carlo [RJMCMC] method. When using the RJMCMC method in a regime-switching situation, the chain determines the optimal number of regimes by jumping between various possibilities. This method gives each model its posterior probability of being the best. After an overview of the methodology, the authors apply it to various datasets and discuss the applications in modern actuarial science.

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 theme of this paper relates to solving portfolio selection problems using linear programming. The authors extend the linear optimization framework for Conditional Value-at-Risk-based portfolio selection problems to optimization over a more general class of risk measure known as the class of Coherent Distortion Risk Measure. From Actuarial Research Clearing House, 2012 Vol. 1.

Description: The Product Development Section, the Financial Reporting Section, the Modeling Section and the Committee on Life Insurance Research are pleased to present new research on modeling and forecasting cause-of-death mortality. Authored by a Milliman team led by Alexandre Boumezoued, the report develops a framework for forecasting cause specific death rates. Included also is a Cause of Deaths Forecasting Tool and a user guide for the tool.

Description: This document provides answers to the exercises in the report titled Complexity Science – an introduction and invitation for actuaries:.

Description: The subject of this paper is a financial model for pension planning purposes. The focus of the model is from an individual's point of view with the idea to assist him/her in planning for a financially secure retirement income. The model is dynamic and stochastic, operates in nominal and real terms, and goes beyond the age of retirement. From Actuarial Research Clearing House 1981, Vol. 1.

Description: Discusses the Canadian Institute of Actuaries' disability morbidity study to update the 1964 CDT and explains the techniques used to interpolate incidence rates from the 1964 CDT.

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 paper presents the fundamentals of creating and using projections. From Transactions of Society of Actuaries 1950, Vol. 2, No. 4.