Daniel Nordman
Iowa State University, IA·Mathematics & Statistics
Many statistical analyses in science and industry require choosing a model that describes how the data behave. That choice is often uncertain and hard to make in practice, and conclusions drawn from a wrong model can be unreliable or misleading.
- Fields
Mathematics & Statistics
- Topics
bootstrap
resampling methods
subsampling
- Grants
- 1 known grant, through Jul 2028
What they work on
- Develop the blended bootstrap, a general strategy that merges the strengths of subsampling and the bootstrap, two different ways of re-using data to measure uncertainty, for more accurate inference on complex data.
- Create more flexible empirical likelihood methods for time series and spatial data by combining data-transformation and data-blocking forms of empirical likelihood with new bootstrap schemes.
- Establish 'bridged' resampling methods that remain valid under mild conditions on the data, so they can be used without risky model choices in applications such as environmental and economic data analysis.
More about the research
Model-free methods offer a way around this problem by drawing conclusions with few assumptions about the data. Such tools matter in fields that face many different kinds of data, including environmental science, economics, finance, geology and astronomy.
Why it matters
Researchers in fields such as environmental science, economics, finance, geology and astronomy often must analyze complex, interrelated data without knowing the right model. Resampling tools that remain valid under mild conditions could give them more reliable error bars and conclusions. They would also reduce the risk of misleading results from a poorly chosen model.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Haihan Yu
University of Rhode Island, RI·Mathematics & Statistics
Develop blended bootstrap techniques that merge subsampling (re-using smaller portions of the data) with the bootstrap (redrawing samples from the data), giving a general strategy for more accurate statistical inference.
Minge Xie
Rutgers University-New Brunswick, NJ·Mathematics & Statistics
Develop PCSI (Predictability-Computability-Stability Inference) for binary and multi-class classification, screening many algorithms by predictive performance and using bootstrap resampling to improve uncertainty estimates, robustness and accuracy.
Matias Cattaneo
Princeton University, NJ·Mathematics & Statistics
Demonstrate the empirical and theoretical limitations of binned regression models applied to mixed-frequency panel data in climate economics, showing when they can lead to incorrect conclusions.
The school
Iowa State University: 23 labs on PI Pages, $462M a year on research, 327 research doctorates a year.
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