False Discovery Control in Non-Standard Settings — NSF Award to University of Washington (WA, $225,000)
Controlling the false positive error in model selection is a prominent paradigm for gathering evidence in data-driven science. In model selection problems such as variable selection and graph estimation, models are characterized by an underlying Boolean structure, such as the presence or absence of a variable or an edg
| Award title | False Discovery Control in Non-Standard Settings |
|---|---|
| Award ID | 2413074 |
| Awardee | University of Washington |
| City | SEATTLE |
| State | WA |
| Amount obligated | $225,000 |
| Principal investigator | Armeen Taeb |
| Program | STATISTICS |
| Start date | 07/01/2024 |
| Abstract | Controlling the false positive error in model selection is a prominent paradigm for gathering evidence in data-driven science. In model selection problems such as variable selection and graph estimation, models are characterized by an underlying Boolean structure, such as the presence or absence of a variable or an edge. Therefore, false positive error or false negative error can be conveniently specified as the number of variables/edges that are incorrectly included or excluded in an estimated |
| Source | NSF Awards |
$799/mo
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