Description: This paper attempts a model for a mix of DB and DC retirement plans that meets the dual objectives of predictability of employer cash contributions, and sufficient employee retirement benefit. From Current Pension Actuarial Practice in Light of Financial Economics Symposium, M-RS04-1.

Description: In this paper we shall present our results in terms of the estimation of human mortality rates, but the results carry over to other applications. We shall discuss a Bayesian model that encompasses both graduation [smoothing] and prediction.

Description: This paper presents Bayesian models for analysis of claim lag data. The analysis of claim runoff patterns investigate the extraction of information on trend and seasonal patterns in incurred claims. This is investigated on the basis of individual group data, and also relative to a larger aggregation of pooled experience. The models are implemented based on Markov chain Monte Carlo methods. The models are applied to examples using simulated claim lag data. From Actuarial Research Clearing House, 2003, Vol. 1.

Description: This work describes a study undertaken to determine whether the Federal Housing Administration's 'FHA' single-family-home mortgage insurance program for investor 'non-occupant' loans should be modified. The sample data were analyzed using both Bayesian and sample reuse procedures. A commentary on the paper is included.

Description: This paper discusses the Bayesian graduation method of Kimeldorf and Jones and develops methods for managing four technical problems in their method. It also includes a discussion of the paper. From Transactions of Society of Actuaries 1977, Vol. 29.

Description: This paper examines the compatibility of the two models, in the hope that this exploration will prompt a greater cross-fertilization of ideas between the life actuarial branch which favors Bayesian models and the casualty branch which favors Hans Buhlmann models.

Description: In this paper, the Bayesian model of collective risk theory is extended in the sense that a large class of distributions is used instead of a single one. The authors then investigate the sensitivity of variance premium principle when the structure function belongs to that class. According to robust Bayesian methodology, the uncertainty in the prior can be modeled by specifying a class of priors instead of a single one. The authors examine the ranges of the Bayesian premium, also called experience rated premium, when the priors belong to that class. Most of the robust Bayesian procedures developed measures of sensitivity of quantities which can be expressed in terms of posterior expectation [e.g., mean, variance and probability of given sets]. Nevertheless, relatively few papers have been related to measure Bayesian sensitivity of quantities which can be expressed in terms of ratio of two posterior expectations, as occurs in the variance premium principle. Appropriate techniques to solve this situation are considered. Unimodality turns out to be very convenient for modelling subjective beliefs about the risk parameter. The very common Poisson-Gamma model is developed in depth using this paper's methodology.

Description: In this Chapter we present a novel way for estimating aggregated EC figures based on Bayesian copula estimation. Contrary to the classic approach of using a single inter-risk- correlation matrix we derive a probability distribution of possible correlation matrices that enables us to tackle the important issue of parameter uncertainty. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

Description: This article describes a method that can be useful when re-fitting a risk score model to a population of only moderate size. Learn how to use concepts like ridge regression to train a model with partial credibility.