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Monday, October 14 • 3:45pm - 4:00pm
Prediction Models for Integer and Count Data

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We propose a simple yet powerful framework for modeling integer-valued data, such as counts, scores, and rounded data. The integer-valued data are modeled by Simultaneously Transforming And Rounding (STAR) a continuous-valued process, where the transformation may be known or learned from the data. STAR produces a flexible class of integer-valued processes, which can account for zero-inflation, bounded or censored data, and over- or underdispersion. Scalable computation is available via an efficient MCMC algorithm, which provides a mechanism for direct adaptation of successful Bayesian methods for continuous data to the integer-valued data setting. Using the STAR framework, we develop new additive models and Bayesian Additive Regression Trees (BART) for integer-valued data. The predictive and inferential capabilities of STAR are illustrated using a medical utilization dataset and an animal abundance dataset, with exceptional predictive and computational performance.


Daniel Kowal

Presenter, Rice University

Antonio Canale

University of Padova

Monday October 14, 2019 3:45pm - 4:00pm CDT
BRC 103

Attendees (3)