This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) in the amount of $197,007 is supporting collaborative research to develop new statistical theories and methodologies for tackling issues related to false discovery rate control in regression analysis. The research aims to provide novel statistical tools for analyzing complex data from diverse scientific domains such as brain imaging, genome-wide association studies, and atmospheric...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $125,667 Project Grant to Virginia Polytechnic Institute & State University (Virginia Tech) under the Mathematical and Physical Sciences program (CFDA 47.049). The grant will fund collaborative research to develop new theories and methodologies for multiple hypothesis testing on regression analysis, which is critical for analyzing high-dimensional data in the era of big data. The research project will create...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $225,000 Project Grant to The Trustees of the University of Pennsylvania - doing business as Clinical Practices of the University of Pennsylvania. This grant, funded under the NSF's Mathematical and Physical Sciences program (CFDA 47.049), will support the development of improved methodologies for the high-dimensional variable selection problem. The project aims to create innovative statistical techniques to better...
This $299,999 National Science Foundation Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) funds research into distance-based statistical methods for analyzing complex, high-dimensional data. The Washington University is the primary awardee, with work conducted from Oct. 2021 through Jun. 2024. The University of Pennsylvania serves as a subawardee, contributing to study design, implementation, analysis and manuscripts through the work of Dr. Bhaswar B....
This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of innovative statistical methods known as "hunt-and-test procedures." These methods aim to reliably detect meaningful signals in complex data while avoiding false discoveries due to "double dipping" - the unintentional use of the same data for both identifying and testing hypotheses. The project has two primary...
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 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 $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...
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 award of $146,738 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative statistical research on multivariate and functional time series analysis. The research will develop new nonparametric inference procedures that can accommodate a wide range of data dimensionality and require weak assumptions on the data generating processes. The methodology will be disseminated through publications, presentations, and...