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 $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...
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 $149,989 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research to develop statistical models and inference methods for analyzing random point processes. The research will provide tools for analyzing time series of point process data, with applications in fields such as national security, economics, neuroscience, and geosciences. Key activities include developing parameter estimation procedures,...
The National Science Foundation awarded a $350,000 Project Grant to the University of Washington under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to develop novel strategies for constructing optimal statistical estimators using machine learning tools. Over a three-year period ending August 2025, the investigators will study representations of the efficient influence function that can be computed numerically to derive new asymptotically efficient estimators. They...
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....
This federal Project Grant award from the National Science Foundation (NSF) Integrative Activities program (CFDA 47.083) provides $248,285 to the University of Mississippi for a research project on developing new methods to study long-memory linear random fields. The principal investigator will collaborate with a researcher at Michigan State University to investigate kernel and wavelet estimators for density and quadratic entropy functions of these types of random fields, with a focus on...
This $279,983 National Science Foundation project grant supports research at Brown University to develop hybrid statistical and econometric modeling methods. Funded under the NSF Social, Behavioral, and Economic Sciences program, the three-year award beginning August 2022 aims to advance modeling approaches that account for imperfect data measurement and the reality that models approximate rather than perfectly represent the world. The research will modify method-of-moments techniques to...
This $299,965 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences will allow Colorado State University (CSU) to develop and validate a new statistical model and analytical methods for assessing extremal dependence in high-dimensional data. This work aims to improve quantification of joint risks in applications such as finance, insurance, and climate science. The project will include training a graduate student in extreme value analysis techniques...
The National Science Foundation awarded a $249,999 Project Grant to North Carolina State University on May 15, 2021 under the Social, Behavioral, and Economic Sciences program (CFDA 47.075). The grant supports research on imprecise probability and valid statistical inference through April 30, 2024. As part of this effort, Rutgers, The State University will receive a sub-award to contribute to the project. The overarching goal of the NSF program is to promote basic research and education in the...