Zero-inflated regression modeling for continuous data with applications in ecology

with Dr. Becky Tang, Middlebury College

Monday, February 24th
Votey Hall, Room 209
3:00 PM - 4:00 PM

Abstract: 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. The 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 analog 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. 

Bio: Dr. Becky Tang is an Assistant Professor of Statistics at Middlebury College. She earned her PhD in Statistical Science from Duke University (2022) under Drs. Alan Gelfand and James Clark, and holds a BA from Swarthmore College (2018). Previously an NSF Graduate Research Fellow, her work focuses on Bayesian hierarchical modeling in ecological sciences, particularly joint species distribution models. Beyond research, Dr. Tang is committed to making statistics education more accessible to underrepresented students and integrating Bayesian methods into undergraduate teaching.