# Collaborative Research: NSF-SNSF: Tail-robust Analysis of High-dimensional Nonstationary Time Series

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · University of Georgia Research Foundation Inc (GA) · $175,000

## Abstract

High-dimensional time series data arise in many fields such as economics, epidemiology, neuroscience, and social science, where large numbers of measurements are collected over time. These data often exhibit complex patterns, including shifts in behavior and extreme values that violate classical statistical assumptions. This project addresses fundamental challenges in analyzing such time series, especially when they are not stationary and prone to abrupt structural changes. The research in this project aims to develop new methods that are robust to extreme events and better suited to the realities of modern data. By improving the ability to detect and interpret changes in large, evolving systems, this project may be used to support scientific discovery across disciplines. It also provides training opportunities for graduate students, helping build a more data-literate workforce. The project advances the frontiers of science and supports the development of innovative statistical tools that can enhance decision-making in dynamic environments.

The research conducted within the scope of this project develops a new tail-robust statistical framework for the analysis of high-dimensional nonstationary time series. The project focuses on two interrelated goals: (1) to construct robust estimators of autocovariance structures that remain accurate in the presence of outliers and large deviations, and (2) to develop efficient procedures to detect and quantify structural changes over ti

## Key facts

- **NSF award ID:** 2514399
- **Awardee organization:** University of Georgia Research Foundation Inc (GA)
- **SAM.gov UEI:** NMJHD63STRC5
- **PI:** Yuan Ke
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** International Partnerships, Machine Learning Theory, SWITZERLAND
- **Estimated total:** $175,000
- **Funds obligated:** $175,000
- **Transaction type:** Standard Grant
- **Period:** 09/01/2025 → 08/31/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2514399

## Citation

> US National Science Foundation, Award 2514399, Collaborative Research: NSF-SNSF: Tail-robust Analysis of High-dimensional Nonstationary Time Series. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2514399. Licensed CC0.

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