Numerical Construction of Optimal Estimators Using Machine Learning Tools — NSF Award to University of Washington (WA, $175,000)
Optimal statistical procedures make maximal use of available data, making it possible to answer pressing scientific questions more precisely and cost-effectively. These procedures are traditionally derived via analytic calculations that require expert knowledge achieved over many years of training. In this project, the
| Award title | Numerical Construction of Optimal Estimators Using Machine Learning Tools |
|---|---|
| Award ID | 2210216 |
| Awardee | University of Washington |
| City | SEATTLE |
| State | WA |
| Amount obligated | $175,000 |
| Principal investigator | Alex Luedtke |
| Program | STATISTICS |
| Start date | 09/15/2022 |
| Abstract | Optimal statistical procedures make maximal use of available data, making it possible to answer pressing scientific questions more precisely and cost-effectively. These procedures are traditionally derived via analytic calculations that require expert knowledge achieved over many years of training. In this project, the investigators will study two novel strategies for deriving optimal procedures. Compared to existing approaches, these strategies require more expertise in computational methods an |
| Source | NSF Awards |
$799/mo
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