Quantile Regression in the Big Data Regime: Online Learning, Missingness, and Causality — NSF Award to Washington University (MO,
This research project will develop innovative solutions for quantile regression analysis of big data. Big data has become prevalent in modern society due to the exponential growth of digital information. Quantile regression is a powerful statistical tool that goes beyond the average relationship provided by traditional
| Award title | Quantile Regression in the Big Data Regime: Online Learning, Missingness, and Causality |
|---|---|
| Award ID | 2418979 |
| Awardee | Washington University |
| City | SAINT LOUIS |
| State | MO |
| Amount obligated | $349,984 |
| Principal investigator | Nan Lin |
| Program | Methodology, Measuremt & Stats |
| Start date | 07/15/2024 |
| Abstract | This research project will develop innovative solutions for quantile regression analysis of big data. Big data has become prevalent in modern society due to the exponential growth of digital information. Quantile regression is a powerful statistical tool that goes beyond the average relationship provided by traditional regression. However, big data poses fundamental challenges for quantile regression, both statistically and computationally. This project will address those challenges by developin |
| Source | NSF Awards |
$799/mo
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