This Project Grant award for $117,910 from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) aims to develop new statistical estimation methods and algorithms that can efficiently process complex, high-dimensional datasets. The research will focus on three key areas: (1) providing rigorous theoretical guarantees for the performance of high-dimensional statistical estimation techniques, (2) establishing computational limits and efficiencies for modern...
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 National Science Foundation (NSF) Division of Mathematical Sciences awarded a 3-year, $100,000 Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) to Rutgers, The State University located in New Brunswick, New Jersey. The grant supports the development of advanced statistical models and software to predict and assess the likelihood of extreme geopolitical events with quantified uncertainty. The project aims to construct a comprehensive, data-driven prediction...
The National Science Foundation (NSF) awarded a $100,000 Project Grant under its Integrative Activities program (CFDA 47.083) to Rutgers, The State University located in Piscataway, New Jersey. The three-year grant, awarded on August 1, 2023, will fund research to develop novel statistical inference tools and computationally efficient approaches for reinforcement learning in high-dimensional, non-identically distributed data settings. Key focus areas include statistical inference for...
This Project Grant award of $194,675 from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to develop new econometric and statistical methods that allow researchers to draw valid conclusions from complex data in a variety of social, behavioral, and medical science settings. The key products and services to be delivered include: Developing a generally applicable reparameterization procedure for informative...
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...
The National Science Foundation (NSF) awarded a $249,989 project grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of New Hampshire's (UNH) Office of Sponsored Research. The grant supports the development of efficient and robust statistical tools for modeling data with measurement errors, a common challenge in fields like epidemiology and economics. The research aims to improve estimation accuracy and hypothesis testing power across linear, nonlinear, and...
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 $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 $169,977 Project Grant to Texas A&M University under the Mathematical and Physical Sciences program (CFDA 47.049) to support research titled "ROBUST AND EFFICIENT STATISTICAL INFERENCE IN LARGE SCALE SEMI-SUPERVISED SETTINGS." The three-year award, which runs from August 1, 2021 through July 31, 2024, will fund the development of statistical methods to enable robust and efficient inference on large, semi-supervised datasets. As the prime...
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 Bayesian methods in approximate message passing. The project aims to establish theoretical foundations and produce a collection of tools to enable statistical inference in fields such as sociology, economics, neural imaging, and bioinformatics. It is expected these methodological advances will significantly strengthen statistics and data science research.