6 papers · 1 filter
Rashomon-Seeded Annealing for Robust Bayesian Inference in Factorial Designs
Yiyang Fan, Soumyakanti Pan, Tyler H. McCormick
Integrating over model uncertainty in factorial designs via Bayesian model averaging is hindered by the combinatorial explosion of interpretable interaction effects, often yielding…
ipd: An R Package for Conducting Inference on Predicted Data
Stephen Salerno, Jiacheng Miao, Awan Afiaz +5
Summary: ipd is an open-source R software package for the downstream modeling of an outcome and its associated features where a potentially sizable portion of the outcome data has…
What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research
Stephen Salerno, Emily K. Roberts, Belinda L. Needham +4
In this work, we are motivated by the problem of estimating racial disparities in health outcomes, specifically the average controlled difference (ACD) in telomere length between B…
Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models
Kentaro Hoffman, Stephen Salerno, Jeff Leek +1
Large-scale prediction models using tools from artificial intelligence (AI) or machine learning (ML) are increasingly common across a variety of industries and scientific domains.…
Dempster-Shafer P-values: Thoughts on an Alternative Approach for Multinomial Inference
Kentaro Hoffman, Kai Zhang, Tyler McCormick +1
In this paper, we demonstrate that a new measure of evidence we developed called the Dempster-Shafer p-value which allow for insights and interpretations which retain most of the s…
Do We Really Even Need Data?
Kentaro Hoffman, Stephen Salerno, Awan Afiaz +2
As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e.g. rising costs, declining survey response rat…