Reinforcement learning (RL) is a computational approach to reward-driven learning in sequential decision problems. It implements the discovery of optimal actions by learning from an agent interacti…
We recognize the careful reading of and thought-provoking commentary on our work by Rice and Lumley. Further, we appreciate the opportunity to respond and clarify our position regarding the three p…
"Comment on “A Case for Nonparametrics” by Bower et al.." The American Statistician, 77(2), p. 220
We provide a case study for motivating and teaching nonparametric statistical inference alongside traditional parametric approaches. The case consists of analyses by Bracht et al. who use analysis …
Hierarchical clustering is one of the standard methods taught for identifying and exploring the underlying structures that may be present within a dataset. Students are shown examples in which the …
When releasing record-level data containing sensitive information to the public, the data disseminator is responsible for protecting the privacy of every record in the dataset, simultaneously prese…
Conventional wisdom dispersed by fans and coaches in the stands at almost any high school track meet suggests female athletes typically peak around 10th grade or earlier (15 years of age), particul…
Modern wearable monitors and laboratory equipment allow the recording of high-frequency data that can be used to quantify human movement. However, currently, data analysis approaches in these domai…
Overfitting in linear regression is broken down into two main causes. First, the formula for the estimator includes “forbidden knowledge” about training observations’ residuals, and it loses …
We present an efficient method of calculating exact confidence intervals for the hypergeometric parameter representing the number of “successes,” or “special items,” in the population. The …