Event Date
Speaker: Will Townes, Assistant Professor, Dept of Statistics and Data Science, Carnegie Mellon University
Title: "A novel class of mixed Poisson distributions and wastewater-based epidemiology"
Abstract: Mixed Poisson families are widely used to model count data with overdispersion, zero inflation, or heavy tails in a variety of applications including finance, biology, and the physical sciences. The Poisson rate is typically assigned a nonnegative-valued mixing distribution. Surprisingly, it is also possible for the mixing distribution to have negative support. For example, the Hermite distribution is analogous to mixing a Poisson with an unconstrained Gaussian and can be derived using generating functions so long as constraints on the natural parameter are satisfied. I will give a general characterization of this unusual class as well as several concrete examples. Time permitting, I will also briefly present some recent applied work in wastewater-based epidemiology involving the pepper mild mottle virus biomarker.
Faculty website (links to CMU): https://www.cmu.edu/dietrich/statistics-datascience/research/members/will-townes.html