# Novel Bayesian Frameworks for Measurement Error Problems in Complex Multivariate Data

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · University of Texas at Austin (TX) · $175,000

## 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 typically handle such problems under overly simplistic conditions, limiting their usefulness in complex, real-world, multivariate settings. By developing more flexible, principled methods that address realistic measurement error scenarios, this project aims to promote the national interest by supporting more accurate, data-driven decision-making in health, policy, and other applied fields. The project also contributes to workforce development in statistics and data science through graduate training, ensuring that students gain experience with modern data-driven research approaches.

Technically, the project develops novel Bayesian hierarchical frameworks for multivariate density deconvolution and related regression-with-errors-in-variables problems. It introduces covariate-informed density deconvolution methods that flexibly allow both the variables of interest and their measurement errors to vary with associated predictors. These methods incorporate automatic covariate selection and permit 

## Key facts

- **NSF award ID:** 2515902
- **Awardee organization:** University of Texas at Austin (TX)
- **SAM.gov UEI:** V6AFQPN18437
- **PI:** Abhra Sarkar
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** Machine Learning Theory
- **Estimated total:** $175,000
- **Funds obligated:** $175,000
- **Transaction type:** Standard Grant
- **Period:** 08/15/2025 → 07/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2515902

## Citation

> US National Science Foundation, Award 2515902, Novel Bayesian Frameworks for Measurement Error Problems in Complex Multivariate Data. Retrieved via AI Analytics 2026-06-06 from https://api.ai-analytics.org/grant/nsf/2515902. Licensed CC0.

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