The University of California, Davis was awarded a $335,849 project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) to develop advanced statistical methodology for analyzing complex data in metric spaces from July 1, 2023 to June 30, 2026. Specifically, UC Davis will conduct research to establish regression models, testing and estimation methods, and computational tools for statistical analysis of random objects that take values in...
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...
The University of California, Davis was awarded a three-year $275,000 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The grant supports research to develop innovative methodologies and theoretical foundations for analyzing high-dimensional and non-Euclidean data commonly encountered in fields such as biology, social science, computer science, and astronomy. Key products include...
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...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $200,000 Project Grant to The University Corporation, a non-profit organization located in Northridge, CA. The grant, funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), focuses on developing new statistical modeling and data resampling methods to address challenges posed by incomplete, missing, and fragmented observations in large datasets. Key objectives include: Advancing...
This National Science Foundation Project Grant of $220,000 supports research into statistical modeling methods for large, complex datasets. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the University of California, San Francisco will develop new Bayesian regression techniques using random data compression matrices. These approaches aim to enable efficient, scalable inference and prediction from high-dimensional biomedical data sources like brain imaging, genetics,...
This $152,997 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research at the University of Southern California (USC) on computer-intensive statistical inference methods for high-dimensional and massive datasets. The project aims to develop efficient, scalable, and statistically robust inferential procedures for two classical problems - change point detection/identification and computationally-aware statistical...
This $289,999 National Science Foundation project grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), will fund the development of improved statistical methods, algorithms, and theory for estimation and inference with high-dimensional data at Rutgers, The State University from July 2022 through June 2025. Key products include new statistical methods for regularized estimation, de-biased statistical inference including confidence intervals and regions, and empirical...