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Bayesian Negative Binomial Regression Model with Unobserved Covariates for Predicting the Frequency of North Atlantic Tropical Storms by Xun Li; Joyee Ghosh; Gabriele Villarini is a Mathematics article available to read on EtoBox.

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Predicting the annual frequency of tropical storms is of interest because it can provide basic information towards improved preparation against these storms. Sea surface temperatures (SSTs) averaged over the hurricane season can predict annual tropical cyclone activity well. But predictions need to be made before the hurricane season when the predictors are not yet observed. Several climate models issue forecasts of the SSTs, which can be used instead. Such models use the forecasts of SSTs as surrogates for the true SSTs. We develop a Bayesian negative binomial regression model, which makes a distinction between the true SSTs and their forecasts, both of which are included in the model. For prediction, the true SSTs may be regarded as unobserved predictors and sampled from their posterior predictive distribution. We also have a small fraction of missing data for the SST forecasts from the climate models. Thus, we propose a model that can simultaneously handle missing predictors and variable selection uncertainty. If the main goal is prediction, an interesting question is should we include predictors in the model that are missing at the time of prediction? We attempt to answer this

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Author

Xun Li; Joyee Ghosh; Gabriele Villarini

Publisher

Informa UK Limited

Published

2022

Language

EN

Field

Mathematics (Physical Sciences)