This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program provides $180,000 to North Carolina State University from September 1, 2022 through August 31, 2025. The funding supports research to develop novel modeling and Bayesian analysis techniques for high-dimensional time series data. Specifically, the awardees will create a framework to represent multi-dimensional time series data as independent latent time series, allowing for more accurate...
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 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program provides $146,738 to The Washington University in St. Louis for a collaborative research project titled "Statistical Inference for Multivariate and Functional Time Series via Sample Splitting". The research aims to develop new nonparametric inference procedures that can accommodate a wide range of data dimensionality and make weak assumptions about the data...
This Project Grant from the National Science Foundation's Mathematical and Physical Sciences program provides $109,999 to support research at the University of Maryland Baltimore County on novel modeling and Bayesian analysis of high-dimensional time series data. The three-year award beginning September 2022 will fund the development of a framework to represent multi-dimensional time series data as independent stationary latent processes modeled with unspecified spectral densities. The...
The National Science Foundation Division of Mathematical Sciences awarded a $110,000 Project Grant to the University of Florida Division of Sponsored Research under the Mathematical and Physical Sciences federal grant program (CFDA 47.049). The three-year award will support the development of novel modeling and Bayesian analysis methods for high-dimensional time series data. Specifically, the principal investigators will create a framework to represent multi-dimensional time series data as a...
This award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $174,344 in funding to The Washington University in St. Louis to conduct collaborative research on statistical modeling and inference for object-valued time series. The key objectives are to: (1) develop a new autoregressive model and tools for distributional time series analysis, (2) create new specification testing procedures for distributional time series, and (3)...
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...
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 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,...
The National Science Foundation (NSF) Division of Mathematical Sciences awarded a $173,740 Project Grant to Florida State University (FSU) Sponsored Research Administration Division to enhance methods for analyzing temporally observed or time-indexed multi-sample data across diverse fields like environmental sciences, epidemiology, and economics. The 3-year grant, starting September 1, 2023, aims to develop innovative statistical techniques to study data that does not fit conventional univariate...