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

• Main Authors: Li, Xun, Ghosh, Joyee, Villarini, Gabriele
• Format: Text
• Language: unknown
• Published: Taylor & Francis 2022
• Subjects: Environmental Sciences not elsewhere classified; Ecology; FOS Biological sciences; Biological Sciences not elsewhere classified; Information Systems not elsewhere classified; Mathematical Sciences not elsewhere classified; Plant Biology; North Atlantic
• Online Access: https://dx.doi.org/10.6084/m9.figshare.19690382; https://tandf.figshare.com/articles/journal_contribution/Bayesian_negative_binomial_regression_model_with_unobserved_covariates_for_predicting_the_frequency_of_north_atlantic_tropical_storms/19690382

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 ...