Becky Tang: Zero-inflated regression modelling for continuous data with applications in ecology

A frequent challenge encountered in real-world applications is data having a high proportion of zeros. Focusing on ecological abundance data, much attention has been given to zero-inflated count data. Statistical models for non-negative continuous abundance data with an excess of zeros are rarely discussed. Work presented here considers the creation of a point mass at zero through a left-censoring approach or through a hurdle approach. Using a Bayesian approach, we incorporate both mechanisms to capture the analogue of zero-inflation for count data. Time permitting, we will also discuss zero-inflated modeling for multivariate abundance data. Applications may include percent cover of vegetation, tree biomass using FIA data, and insect abundance from urban streams in New England.