This Project Grant award of $146,738.00 from the National Science Foundation's (NSF) Division of Mathematical Sciences, under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program, supports collaborative research on statistical inference for multivariate and functional time series analysis. The research project will develop a new unified framework based on sample splitting and self-normalization techniques to accommodate a wide range of data dimensionality and generate...
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 $169,999 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research at the University of California, Davis (UC Davis) to advance innovative nonparametric data analysis techniques. The project aims to conduct comprehensive statistical and computational analyses to push the boundaries of modern nonparametric statistical inference, with potential applications in areas like nonparametric latent...
The National Science Foundation Division of Mathematical Sciences awarded $299,997 under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to the University of California, Davis for a three-year project grant completing June 2025. The award will support research involving the development of new statistical testing methods and deep learning techniques for functional data analysis. Specifically, the university will conduct research projects to create a general framework...
This federal Project Grant award of $174,344, provided by the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program, supports collaborative research to develop new statistical models and inference methods for analyzing object-valued time series data. The research aims to advance the state-of-the-art in this emerging field with the following objectives: (1) developing a new autoregressive model and supporting tools...
This Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $175,000 to the University of Georgia Research Foundation, Inc. to initiate a new paradigm for statistical inference of high-dimensional time series data. The project aims to develop self-normalized inference methods that can quantify the accumulative uncertainty of high-dimensional data collected over time, which has been a challenge with existing techniques....
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 $155,372 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will fund research to develop advanced statistical methods for extracting insights from high-dimensional, high-frequency "big data." The University of Illinois, Chicago, as the prime awardee, will focus on four key areas: 1) advancing contiguity theory to enable more robust statistical analysis of noisy, high-frequency data; 2) exploring time-varying...
The University of California, San Diego (UCSD) received a $300,000 Project Grant award from the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) program. The grant supports the development of advanced computational and statistical methods for analyzing complex, dependent data from diverse scientific domains such as climate, economics, and neuroimaging. Key research thrusts include subsampling and resampling techniques for large datasets, robust...
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,...