Description: Abstract for a paper that develops links between credibility theory in actuarial science and longitudinal data models in statistics. The paper demonstrates that many additive credibility models can be expressed as special cases of the longitudinal data model.

Description: In this paper we use expectations of different random variables to define the notions of retrospective, prospective, individual, and aggregate net premiums for plans with contingent obligations.

Description: This paper addresses the extension of the theory of life contingencies to a stochastic interest environment and its application to solvency valuation and surplus. A discussion of the paper follows. From Transactions of Society of Actuaries 1990, Vol. 42.

Description: This summary is intended to provide the pension actuary with guidance in interpreting and using the termination and retirement information in the 2003 SOA Pension Plan Termination and Retirement Study. The tables in the study were developed based on 1.7 million life years of data from 112 plans collected primarily from actuarial consulting firms for the period 1995 – 2000.

Description: This paper introduces actuaries to the concept of 'copulas,' a tool for understanding relationships among multivariate outcomes. A copula is a function that links univariate marginals to their full multivariate distribution. Recently, there has been a rapidly developing literature on the statistical properties and applications of copulas. This paper explores some of these practical applications, including estimation of joint life mortality and multidecrement models. In addition, the paper describes basic properties of copulas, their relationships to measures of dependence and several families of copulas that have appeared in the literature. This paper will also be useful to those who wish to use copulas for statistical inference. Statistical inference procedures are illustrated using insurance company data on losses and expenses. For this data, the paper shows how to fit copulas and describes their usefulness by pricing a reinsurance contract and estimating expenses for pre-specified losses.

Description: Automobile insurers classify the risks that they underwrite in order to subdivide the portfolio into classes of risks with similar profiles. While some insurers may have sufficient historical data, several others may not have significant volume of experience data in order to produce reliable claims predictions to help enhance their risk classification systems. A database containing a pooled experience of several insurers thereby helps to produce a more fair, reliable, and equitable premium structure for all risks concerned. This paper uses multilevel models to analyze the data on claim counts provided by the General Insurance Association of Singapore, an organization consisting of most of the general insurers in Singapore. The multilevel nature of the data is due to the following: a certain vehicle is observed over a period of years and is insured by a particular insurance company under a certain 'fleet' policy. The authors show how intercompany data lead to a priori premiums and a posteriori corrections to these initial premiums. Specific focus is made in understanding the intercompany effects using various count distribution models [Poisson, negative binomial, zero–inflated and hurdle Poisson]. The performance of these various models is compared the authors also investigate how to use the historical claims of a company, fleet and/or vehicle in order to correct for the premium initially set.