Bayesian negative binomial regression model with unobserved covariates for predicting the frequency of north atlantic tropical storms

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 question and demonstrate that our model can provide gains in prediction.

Automated summary

Predicting the annual frequency of tropical storms is crucial for better preparedness. This study develops a Bayesian negative binomial regression model that distinguishes between true sea surface temperatures (SSTs) and their forecasts, and uses the posterior predictive distribution for prediction. The model can handle missing predictors and variable selection uncertainty simultaneously. Results demonstrate that including missing predictors in the model can improve prediction performance.