1 citations · 1 across the 5 of their papers we have counts for
6 papers
Prediction-powered inference for time series across space
Shahzar Rizvi, David Burt, Vishwak Srinivasan +3
The following motif is common in spatiotemporal settings: we have a sequence of covariate and label pairs observed for a relatively short, recent time period. We have access to unl…
Wrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification
David R. Burt, Renato Berlinghieri, Tamara Broderick
Scientists are often interested in estimating an association between a covariate and a binary- or count-valued response. For instance, public health officials are interested in how…
Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association
David R. Burt, Renato Berlinghieri, Stephen Bates +1
Estimating associations between spatial covariates and responses - rather than merely predicting responses - is central to environmental science, epidemiology, and economics. For i…
Are Hourly PM2.5 Forecasts Sufficiently Accurate to Plan Your Day? Individual Decision Making in the Face of Increasing Wildfire Smoke
Renato Berlinghieri, David R. Burt, Paolo Giani +2
Wildfire frequency is increasing as the climate changes, and the resulting air pollution poses health risks. Just as people routinely use hourly weather forecasts to plan their day…
Approximations to worst-case data dropping: unmasking failure modes
Jenny Y. Huang, David R. Burt, Yunyi Shen +2
A data analyst might worry about generalization if dropping a very small fraction of data points from a study could change its substantive conclusions. Checking this non-robustness…
Consistent Validation for Predictive Methods in Spatial Settings
David R. Burt, Yunyi Shen, Tamara Broderick
Spatial prediction tasks are key to weather forecasting, studying air pollution impacts, and other scientific endeavors. Determining how much to trust predictions made by statistic…