Self-Normalized Inference for High-Dimensional Time Series — NSF Award to University of Georgia Research Foundation Inc (GA, $175,
The project aims to initiate a new paradigm for statistical inference of high-dimensional time series. High-dimensional time series refer to a sequence of large dimensional data collected over time, and examples include large panel data in economics, functional magnetic resonance imaging data in neuroscience, stock pri
| Award title | Self-Normalized Inference for High-Dimensional Time Series |
|---|---|
| Award ID | 2412661 |
| Awardee | University of Georgia Research Foundation Inc |
| City | ATHENS |
| State | GA |
| Amount obligated | $175,000 |
| Principal investigator | Ting Zhang |
| Program | STATISTICS |
| Start date | 08/01/2024 |
| Abstract | The project aims to initiate a new paradigm for statistical inference of high-dimensional time series. High-dimensional time series refer to a sequence of large dimensional data collected over time, and examples include large panel data in economics, functional magnetic resonance imaging data in neuroscience, stock price data for a large set of companies in finance, cellular usage data over time for a large number of users in telecommunication, high-resolution spatio-temporal data in climate sci |
| Source | NSF Awards |
$799/mo
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