Novel Bayesian Frameworks for Measurement Error Problems in Complex Multivariate Data — NSF Award to University of Texas at Austin
This project develops new statistical tools to address a common and important challenge in scientific research: drawing reliable conclusions from data in which observations on variables of interest are imprecise and contaminated by measurement errors. In many real-world studies, from nutrition and health research to as
| Award title | Novel Bayesian Frameworks for Measurement Error Problems in Complex Multivariate Data |
|---|---|
| Award ID | 2515902 |
| Awardee | University of Texas at Austin |
| City | AUSTIN |
| State | TX |
| Amount obligated | $175,000 |
| Principal investigator | Abhra Sarkar |
| Program | STATISTICS |
| Start date | 08/15/2025 |
| Abstract | This project develops new statistical tools to address a common and important challenge in scientific research: drawing reliable conclusions from data in which observations on variables of interest are imprecise and contaminated by measurement errors. In many real-world studies, from nutrition and health research to astronomy, neuroimaging, and social science, measurements are often noisy, making it difficult to identify meaningful patterns or relationships. Existing statistical methods typicall |
| Source | NSF Awards |
$799/mo
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