69 citations · 282 across the 11 of their papers we have counts for
11 papers
Analyzing Students' Statistics Writing Before and After the Emergence of Large Language Models
Sara Colando, Erin Franke, Gordon Weinberg +1
The ability to communicate statistical results to domain experts and stakeholders is an important goal of the undergraduate statistics and data science curriculum. However, as larg…
Do LLMs write like humans? Variation in grammatical and rhetorical styles
Alex Reinhart, Ben Markey, Michael Laudenbach +4
Large language models (LLMs) are capable of writing grammatical text that follows instructions, answers questions, and solves problems. As they have advanced, it has become difficu…
Big data, big problems: Responding to "Are we there yet?"
Alex Reinhart, Ryan Tibshirani
Bradley et al. (arXiv:2106.05818v2), as part of an analysis of the performance of large-but-biased surveys during the COVID-19 pandemic, argue that the data defect correlation prov…
Expanding the scope of statistical computing: Training statisticians to be software engineers
Alex Reinhart, Christopher R. Genovese
Traditionally, statistical computing courses have taught the syntax of a particular programming language or specific statistical computation methods. Since the publication of Nolan…
Think-aloud interviews: A tool for exploring student statistical reasoning
Alex Reinhart, Ciaran Evans, Amanda Luby +6
Think-aloud interviews have been a valuable but underused tool in statistics education research. Think-alouds, in which students narrate their reasoning in real time while solving…
Self-exciting point processes with spatial covariates: modeling the dynamics of crime
Alex Reinhart, Joel Greenhouse
Crime has both varying patterns in space, related to features of the environment, economy, and policing, and patterns in time arising from criminal behavior, such as retaliation. S…
A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications
Alex Reinhart
Self-exciting spatio-temporal point process models predict the rate of events as a function of space, time, and the previous history of events. These models naturally capture trigg…
Sequential nonparametric tests for a change in distribution: an application to detecting radiological anomalies
Oscar Hernan Madrid Padilla, Alex Athey, Alex Reinhart +1
We propose a sequential nonparametric test for detecting a change in distribution, based on windowed Kolmogorov--Smirnov statistics. The approach is simple, robust, highly computat…
Multiscale spatial density smoothing: an application to large-scale radiological survey and anomaly detection
Wesley Tansey, Alex Athey, Alex Reinhart +1
We consider the problem of estimating a spatially varying density function, motivated by problems that arise in large-scale radiological survey and anomaly detection. In this conte…
Detecting changes in maps of gamma spectra with Kolmogorov-Smirnov tests
Alex Reinhart, Valérie Ventura, Alex Athey
Various security, regulatory, and consequence management agencies are interested in continuously monitoring wide areas for unexpected changes in radioactivity. Existing detection s…
Spatially-Aware Temporal Anomaly Mapping of Gamma Spectra
Alex Reinhart, Alex Athey, Steven Biegalski
For security, environmental, and regulatory purposes it is useful to continuously monitor wide areas for unexpected changes in radioactivity. We report on a temporal anomaly detect…