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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.

Speakers
DK

Daniel Kowal

Presenter, Rice University
AC

Antonio Canale

University of Padova



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

Attendees (3)