Description: This paper outlines a method of parametric modeling that may be used to produce forecasts and forecast intervals that will convey a measure of the reliance that may be placed on the forecasts. Followed by 2 discussion papers submitted

Description: An approach to quantify medical intervention savings when medical claims data is not available by leveraging overlapping measurement techniques.

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

Description: Research report that explores key factors associated with an insurer’s reputational risk and conditions that would generate incentives for insurers to behave in a way that causes loss of their positive reputation.

Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.

Description: 1994 SOA Spring Meeting, Orlando. In this session the panel consider the question of uncertainty in loss reserves including discussion of sources and measurement. The panelists analyzed the same data set from different perspectives and discussed the strengths and weaknesses of the approaches.

Description: Description of the vast variety of predictive analytics topics covered by the Forecasting & Futurism section and introduction to the July 2015 issue of the newsletter.

Description: In this paper, we study a survival regression model known as Cox's proportional Hazards model. We assume that the data available are already grouped and develop a minimum distance estimation procedure that is also applicable when the data are also left or right censored. Asymptotic properties of the estimators are established and the numerical implementation of the method based on an iterated reweighted least squares algorithm is discussed.

Description: The purpose of this paper is to introduce the use of least squares for estimating parameters of mortality curves and discuss the advantages of this method.

Description: Reviewing a text covering multiple statistical modeling topics: e.g., generalized linear models, decision trees, clustering, using the statistical language R.