activity
20212026
collaborators
Showing stat.MEShow all

5 papers · 1 filter

stat.ME2026

Bayesian Mixture Models for Histograms: with Applications to Large Datasets

Richard L. Warr, Fernando A. Quintana, Alessandra Guglielmi +1

In many real-world scenarios, especially those involving privacy constraints or data summarization, data are available only in aggregated forms, such as histograms or frequency tab…

stat.ME2026

Clustering Craters on the Moon with Dysfunctional Families

Nathan Weed, Emily Castleton, Dave Osthus +2

Summaries of craters on terrestrial bodies, such as the number and size distribution, are essential for understanding the history of the Solar System. Identifying craters, however,…

stat.ME2021

Bootstrapping Through Discrete Convolutional Methods

Jared M. Clark, Richard L. Warr

Bootstrapping was designed to randomly resample data from a fixed sample using Monte Carlo techniques. However, the original sample itself defines a discrete distribution. Convolut…

stat.ME2021

The Attraction Indian Buffet Distribution

Richard L. Warr, David B. Dahl, Jeremy M. Meyer +1

We propose the attraction Indian buffet distribution (AIBD), a distribution for binary feature matrices influenced by pairwise similarity information. Binary feature matrices are u…

stat.ME2021

Exact Confidence Intervals for Linear Combinations of Multinomial Probabilities

Katherine A. Batterton, Christine M. Schubert, Richard L. Warr

Linear combinations of multinomial probabilities, such as those resulting from contingency tables, are of use when evaluating classification system performance. While large sample…