Project Grant 2514399
- This National Science Foundation (NSF) Project Grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), provides $25,000.00 to The Pennsylvania State University to conduct collaborative research on developing new statistical methods for analyzing high-dimensional, nonstationary time series data. The research aims to construct robust estimators of autocovariance structures that can accurately handle outliers and large deviations, as well as develop efficient procedures...
- This $150,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports the development of innovative statistical and mathematical methods for time series data analysis. The key objectives of this 2-year project are: a) Developing a variable selection method to identify significant exogenous covariates in autoregressive conditional heteroscedasticity (ARCH) models. b) Designing a novel nonparametric hypothesis test to...
- This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) is funding collaborative statistical research and methodology development for analyzing object-valued time series data. The $174,344 award to The Washington University, which began on January 1, 2025, will support the development of new models, techniques, and theory for statistical inference and change detection in object-valued time series across various scientific and...
- This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will fund research to develop new statistical and computational methods to enhance the reliability of data analysis in modern, large-scale datasets, particularly in the era of AI. The key areas of focus include: (1) analyzing the robustness of manifold and deep learning algorithms for high-dimensional, noisy, and nonlinear data; (2) developing statistical theory...
- This Project Grant award of $246,755.00 from the National Science Foundation (NSF) Directorate for Mathematical and Physical Sciences (CFDA 47.049 - Mathematical and Physical Sciences) to the Research Foundation of the City University of New York (RFCUNY) will develop advanced statistical tools capable of analyzing complex modern time series data. The goal is to uncover hidden signals in vast volumes of data from fields such as biomedicine, economics, and finance that could lead to better...
- The National Science Foundation (NSF) awarded a $240,000 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program to the University of California, Davis. This 3-year award from July 1, 2025 to June 30, 2028 will support research on nonlinear functional time series analysis. The project aims to develop statistical methods and forecasting algorithms for analyzing complex data observed as functions or curves, such as yield curves used in economics. Key outcomes...
- This $220,000 National Science Foundation project grant supports the development of new statistical inference methodologies for multivariate and functional time series analysis at Texas A&M University from July 2022 through June 2025. The award is funded through the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which supports advancing scientific knowledge and understanding in these fields. Specifically, the university researchers will create a unified framework...
- This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program provides $180,000 to North Carolina State University from September 1, 2022 through August 31, 2025. The funding supports research to develop novel modeling and Bayesian analysis techniques for high-dimensional time series data. Specifically, the awardees will create a framework to represent multi-dimensional time series data as independent latent time series, allowing for more accurate...
- This three-year, $300,000 project grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) will support the development of statistical methods for modeling nonstationary time series data. Specifically, the awardee Cornell University will develop estimation and inference methods for a nonstationary graphical model framework called Nonstationary Graphical Models (NonstGM). This framework captures nonstationary...
- The National Science Foundation awarded a $252,937 Project Grant under its Mathematical and Physical Sciences program (CFDA 47.049) to the University of Chicago. The purpose of this 3-year grant, which runs from September 1, 2023 to August 31, 2026, is to enhance statistical methods for analyzing temporally observed, multi-sample data in fields such as environmental science, epidemiology, and economics. The research team will develop innovative approaches to estimate and infer trends in data...
This $150,000 Project Grant award from the National Science Foundation's (NSF) Office of International Science and Engineering (CFDA 47.079) supports collaborative research to develop robust statistical methods for analyzing high-dimensional, nonstationary time series data. The research aims to construct reliable estimators of autocovariance structures that can accommodate outliers and structural changes, enabling more accurate detection and quantification of shifts in complex, evolving systems. Key research goals include: 1) building robust estimators for autocovariance in the presence of heavy-tailed distributions and 2) developing efficient change-point detection procedures for nonstationary time series. This work will advance the frontiers of statistical methods for high-dimensional data analysis and enable scientific discoveries across disciplines like economics, epidemiology, and neuroscience. The award, with a performance period from September 1, 2025 to August 31, 2028, will also provide training opportunities to help build a more data-literate workforce.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $150.0k | 8/29/25 |