arXiv subjects

Rebecca Nugent

Publications and source records attributed to Rebecca Nugent.

5 recordsLinked to original sources

arXiv2026

"Rebuilding" Statistics in the Age of AI: A Town Hall Discussion on Culture, Infrastructure, and Training

This article presents the full, original record of the 2024 Joint Statistical Meetings (JSM) town hall, "Statistics in the Age of AI," which convened leading statisticians to discuss how the field is evolving in response to advances in artificial intelligence, foundation models, large-scale empirical modeling, and data-intensive infrastructures. The town hall was structured around open panel discussion and extensive audience Q&A, with the aim of eliciting candid, experience-driven perspectives rather than formal presentations or prepared statements. This document preserves the extended exchanges among panelists and audience members, with minimal editorial intervention, and organizes the conversation around five recurring questions concerning disciplinary culture and practices, data curation and "data work," engagement with modern empirical modeling, training for large-scale AI applications, and partnerships with key AI stakeholders. By providing an archival record of this discussion, the preprint aims to support transparency, community reflection, and ongoing dialogue about the evolving role of statistics in the data- and AI-centric future.

stat.ML↗

arXiv2022

Think-aloud interviews: A tool for exploring student statistical reasoning

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 problems, differ in important ways from other types of cognitive interviews and related education research methods. Beyond the uses already found in the statistics literature -- mostly validating the wording of statistical concept inventory questions and studying student misconceptions -- we suggest other possible use cases for think-alouds and summarize best-practice guidelines for designing think-aloud interview studies. Using examples from our own experiences studying the local student body for our introductory statistics courses, we illustrate how research goals should inform study-design decisions and what kinds of insights think-alouds can provide. We hope that our overview of think-alouds encourages more statistics educators and researchers to begin using this method.

stat.OT↗

arXiv2021

Clustering Students and Inferring Skill Set Profiles with Skill Hierarchies

Cognitive diagnosis models (CDMs) are a popular tool for assessing students' mastery of sets of skills. Given a set of $K$ skills tested on an assessment, students are classified into one of $2^K$ latent skill set profiles that represent whether they have mastered each skill or not. Traditional approaches to estimating these profiles are computationally intensive and become infeasible on large datasets. Instead, proxy skill estimates can be generated from the observed responses and then clustered, and these clusters can be assigned to different profiles. Building on previous work, we consider how to optimally perform this clustering when not all $2^K$ profiles are possible, e.g. because of hierarchical relationships among the skills, and when not all possible profiles are present in the population. We compare hierarchical clustering and several k-means variants, including semisupervised clustering using simulated student responses. The empty k-means algorithm paired with a novel method for generating starting centers yields the best overall performance.

stat.AP↗

arXiv2020

Learning Social Networks from Text Data using Covariate Information

Describing and characterizing the impact of historical figures can be challenging, but unraveling their social structures perhaps even more so. Historical social network analysis methods can help and may also illuminate people who have been overlooked by historians but turn out to be influential social connection points. Text data, such as biographies, can be a useful source of information about the structure of historical social networks but can also introduce challenges in identifying links. The Local Poisson Graphical Lasso model leverages the number of co-mentions in the text to measure relationships between people and uses a conditional independence structure to model a social network. This structure will reduce the tendency to overstate the relationship between "friends of friends", but given the historical high frequency of common names, without additional distinguishing information, we can still introduce incorrect links. In this work, we extend the Local Poisson Graphical Lasso model with a (multiple) penalty structure that incorporates covariates giving increased link probabilities to people with shared covariate information. We propose both greedy and Bayesian approaches to estimate the penalty parameters. We present results on data simulated with characteristics of historical networks and show that this type of penalty structure can improve network recovery as measured by precision and recall. We also illustrate the approach on biographical data of individuals who lived in early modern Britain, targeting the period from 1500 to 1575.

stat.AP↗

arXiv2010

Stability of Density-Based Clustering

High density clusters can be characterized by the connected components of a level set $L(λ) = \{x:\ p(x)>λ\}$ of the underlying probability density function $p$ generating the data, at some appropriate level $λ\geq 0$. The complete hierarchical clustering can be characterized by a cluster tree ${\cal T}= \bigcup_λ L(λ)$. In this paper, we study the behavior of a density level set estimate $\widehat L(λ)$ and cluster tree estimate $\widehat{\cal{T}}$ based on a kernel density estimator with kernel bandwidth $h$. We define two notions of instability to measure the variability of $\widehat L(λ)$ and $\widehat{\cal{T}}$ as a function of $h$, and investigate the theoretical properties of these instability measures.

stat.ML↗