Project Grant 2514400
- 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....
- This Project Grant award of $179,999 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports comprehensive statistical and computational analyses with the goal of advancing innovative nonparametric data analysis techniques. The research aims to push the boundaries of modern nonparametric statistical inference and develop methodologies applicable to areas such as latent variable models, time series analysis, and sequential nonparametric...
- 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...
- 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...
- 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 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 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences Program (CFDA 47.049) focuses on developing new frequency domain modeling techniques for analyzing high-dimensional time series data. The $177,718 award to Southern Methodist University (SMU) will fund research to create a new modeling framework that enables dimension reduction and correlation analysis of large, complex time series datasets across disciplines such as neuroscience,...
- 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...
- The National Science Foundation (NSF) awarded a $146,738 Project Grant under the Mathematical and Physical Sciences Federal Grant Program (CFDA 47.049) to The Washington University for the collaborative research project "Statistical Inference for Multivariate and Functional Time Series via Sample Splitting." The project aims to develop novel nonparametric inference procedures that can accommodate high dimensionality and diverse data-generating processes for analyzing multivariate and...
- 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 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 to detect and quantify structural changes over time. This work is expected to enhance the ability to analyze complex, evolving systems across disciplines such as economics, epidemiology, neuroscience, and social science. The project, which runs from September 1, 2025 to August 31, 2028, also provides training opportunities for graduate students to support the development of a data-literate workforce.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $25.0k | 8/29/25 |