Published 01 June 2020
Dr. Saumyadipta Pyne and collaborators (Prof. Benjamin Kedem and Xuze Zhang, University of Maryland) have developed a new statistical framework for real and synthetic data fusion to estimate exceedance probabilities in an observed stream of events with only a few observations. Starting with a baseline distribution, this method can model a dynamic distortion of that original template, and thus, be used for modeling environmental exposures. The study, Estimation of residential radon concentration in Pennsylvania counties by data fusion, was published in 'Applied Stochastic Models in Business and Industry' in June 2020. A follow-up study, Model selection in radon data fusion (PDF), addressed the problem of model selection in such data fusion. Image courtesy: US EPA: Radon.