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Activity Number: 245 - Bayesian Inference in the Life Sciences and Medicine
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #306440
Title: Bayesian Variable Selection and Bayesian Model Averaging for Predicting Environmental Phenomena
Author(s): Joyee Ghosh*
Companies: The University of Iowa
Keywords: Data Augmentation; Linear Regression; Markov Chain Monte Carlo; Median Probability Model; Missing Data; Negative Binomial Regression

Our goal is to predict environmental phenomena, such as the number of tropical storms in a given hurricane season, using both point and interval estimates. We use Bayesian linear regression and Bayesian negative binomial regression models, which can accommodate missing data. Our models incorporate variable selection uncertainty via a stochastic search variable selection prior. Using real data, we illustrate that our models can improve upon the results of some non Bayesian models in the literature. This is joint work with Xun Li and Gabriele Villarini.

Authors who are presenting talks have a * after their name.

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