This Project Grant award of $174,344 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative statistical modeling and inference research for object-valued time series data. The research aims to develop new models, methodology, and theory for analyzing data representing random objects in metric spaces, such as intraday financial asset returns, age-at-death distributions, energy source compositions, and medical imaging data....
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...
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 $240,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports research at the University of California, Davis (UC Davis) to develop a comprehensive framework for analyzing nonlinear and non-Gaussian functional time series data. The research aims to produce new probabilistic results, introduce two novel functional time series models, and develop accompanying inference procedures for statistical analysis. This...
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...
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,...
This $220,000 National Science Foundation project grant supports the development of new statistical inference methodologies for multivariate and functional time series analysis at Texas A&M University from July 2022 through June 2025. The award is funded through the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which supports advancing scientific knowledge and understanding in these fields. Specifically, the university researchers will create a unified framework...
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 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,...
This $200,000 National Science Foundation Project Grant under the Mathematical and Physical Sciences program (CFDA 47.049) will support the development of new statistical methods that incorporate qualitative constraints into semi-parametric models. The awardee, Carnegie Mellon University, will work to create general non-parametric regression estimators that account for subject matter constraints and adapt to the smoothness of the underlying data. Researchers will also explore approaches for...