Description: The Delphi Method is discussed in detail. The method attempts to filter out the consensus bias that can result from societal forces by employing a technique whereby an anonymous collection of opinions is used, supplemented by feedback loops that can be used to influence outcomes.

Description: Welcome to our Genetic Algorithm shell for Excel.

Description: This article explores the use of Generalized Additive Models and Multivariate Adaptive Regression Splines to model nonlinear relationships in a linear response model. These models use supervised methods to determine the optimal shape of the predictors in the model. Users are provided an example use case so that they may get started right away building models to explore complex relationships in new ways.

Description: This article discusses how to handle semisupervised data. This type of data might have a large number of observations, yet a small number of labels.

Description: A list of accomplishments from 2016 for the Predictive Analytics & Futurism section.

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

Description: Issue Introduction and editorial on how PAF tools and techniques can ease the angst of competing in the financial risk arena.

Description: This article compares three potential approaches to determining new coefficients for health-based risk scoring models: full recalibration, p-value credibility weighting, and penalized ridge regression.

Description: A description of actuaries who also make art.

Description: In How Significant Is Statistical Significance? The author discusses the p-value that is often used to determine significance. Sometimes we place too much reliance upon canned metrics and lose the ability to notice when something is mathematically sound but not sensible. The article provides three simple examples that require no complicated mathematics to follow yet provide a basis for some deep thought about statistical significance versus substantive significance.