Michael Pearce (he/him)


Graduated in 2023
ORCID iD  0000-0002-9313-271X 


On the validity of bootstrap uncertainty estimates in the Mallows-Binomial model
Michael Pearce, Elena A. Erosheva
The Mallows-Binomial distribution is the first joint statistical model for rankings and ratings (Pearce and Erosheva, 2022). Because frequentist estimation of…

Modeling Preferences: A Bayesian Mixture of Finite Mixtures for Rankings and Ratings
Michael Pearce, Elena A. Erosheva
Rankings and ratings are commonly used to express preferences but provide distinct and complementary information. Rankings give ordinal and scale-free…

A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review
Michael Pearce, Elena A. Erosheva
Rankings and scores are two common data types used by judges to express preferences and/or perceptions of quality in a collection of objects. Numerous models…