Description: In this article, we are going to discuss the AR model by making connections to time-dependent ordinary differential equations.

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

Description: Actuaries have the opportunity to transform to a new role that utilizes predictive analytics and new data sources such as epigenetics, to better humankind.

Description: Actuaries provide special subject matter expertise to question (or validate) the results of data analytics models. Other quants often cannot do this.

Description: Chairperson's column for the May 2019 issue of Predictive Analytics & Futurism.

Description: A concise overview of Generative Adversarial Networks.

Description: The article introduces the concept of overfitting in predictive modeling and illustrates how it hinders our ability to predict new data well.

Description: In An Introduction to Incremental Learning, by Qiang Wu and I describe how machine learning is a natural enhancement to predictive analytics through its many useful tools to derive meaning from a lot of otherwise unusable data such as handwriting. Again, there are formulas involved; but we have tried to keep them understandable to the actuary who isn’t already familiar with stochastic gradient descent, perceptrons, and principal component analysis. Incremental learning can help you refine the estimations you derive from more traditional actuarial methods.

Description: Collaborative filtering models drive recommendations provided by Netflix and Amazon. This article uses the same models to explore patients’ medical conditions.

Description: This article gives a brief overview of ways to extend clustering analysis beyond basic methods such a k-means