Description: Python is the leading programming language for data science. Python programming is one skill that actuaries should acquire. It facilitates analytic work, and the available visualization tools help to communicate ideas. In this article, I use a traditional actuarial problem, mortality modeling, as an example.

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

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

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

Description: Regression to the mean is a phenomenon that arises when comparing sequential data points. It states that when an initial observation is extreme or an outlier, future observations will be closer to the average. Health care actuaries need to understand and be able to mitigate the effects of regression to the mean to avoid misapplying predictive models or misstating measurements of historical effects.

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

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

Description: This paper has two related purposes: first, to introduce a new class of Moving Weighted Average MWA graduation formulas where the coefficients are determined in accordance with the relative emphasis placed on smoothness and fit by the graduator, and second, to consider the question regarding what value of n to use when graduating by MWA formula. From the Actuarial Research Clearing House, 1978, Vol.1.