Description: This chapter introduces a few probability distributions that play an important role in understanding and analyzing healthcare data. While theoretical in nature, these distributions model real life if appropriately chosen. Probability distributions are mathematical representations of how statistical data behave.

Description: Second article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

Description: This session is from the 1976 Washington D.C. Meeting and discusses qualitative and quantitative models for forecasting. The session is a general teaching session and not for experts.

Description: An exploration of the seasonality of mortality and how total population mortality is related to flu and pneumonia mortality.

Description: This abstract describes a paper in which several issues surrounding the 2011 paper “Copula Regression”, Variance volume 5-issue1, by Rahul A. Parsa and Stuart A. Klugman, are discussed.

Description: From a session at the Spring regional meeting of the Society of Actuaries held in Atlanta, GA May 24-25, 1999: Year 2000 and Beyond: Society of Actuaries New Course: Applied Modeling Demonstration: Charts 3 & 4 from that presentation

Description: Users of statistical packages should be more aware of the influence that outlying data points can have on their statistical analyses. Robust procedures provide formal methods to spot these outliers and reduce their influence. While a few robust procedures are mentioned in this article, one is emphasized and it is motivated by maximum likelihood estimation to make it seem more natural. In this article, the author will try to provide some background, beginning with maximum likelihood estimation of one parameter, that will hopefully make robust estimation seem natural and rather easy.

Description: An introduction of predictive modeling techniques that are applicable to insurance industry, including generalized linear model (GLM) and classification and regression tree model(CART). Other models such as data clustering, neural network, and support vector machine (SVM), are also briefly discussed.