Computer-intensive methods for dependent and complex data — NSF Award to University of California-San Diego (CA, $300,000)
Ever since the fundamental recognition of the potential role of the computer in modern statistics, the bootstrap and other computer-intensive statistical methods have been developed extensively for inference with independent data. Such methods are even more important in the context of dependent data where the distribut
| Award title | Computer-intensive methods for dependent and complex data |
|---|---|
| Award ID | 2413718 |
| Awardee | University of California-San Diego |
| City | LA JOLLA |
| State | CA |
| Amount obligated | $300,000 |
| Principal investigator | Dimitris Politis |
| Program | STATISTICS |
| Start date | 06/15/2024 |
| Abstract | Ever since the fundamental recognition of the potential role of the computer in modern statistics, the bootstrap and other computer-intensive statistical methods have been developed extensively for inference with independent data. Such methods are even more important in the context of dependent data where the distribution theory for estimators and test statistics may be difficult or impractical to obtain. Furthermore, the recent information explosion has resulted in datasets of unprecedented siz |
| Source | NSF Awards |
$799/mo
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