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 $121,153 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program will support research to develop advanced statistical methods for analyzing complex spatial point process data. The key products and services to be delivered include: Developing nonparametric Bayesian models to reveal hidden spatial homogeneity and heterogeneity within and across univariate and multivariate spatial...
This Project Grant award from the National Science Foundation (NSF) Directorate for Mathematical and Physical Sciences (CFDA 47.049) provides $199,708 to Carleton College to develop new statistical methodology for analyzing complex spatial data. The key products and services to be delivered include: Methods to account for spatial uncertainty in datasets, including those with privacy constraints or geocoding errors. This will involve developing software to implement a constrained spatial...
This three-year project grant from the National Science Foundation's Mathematical and Physical Sciences program, totaling $359,940, will support the development of new statistical models and algorithms for analyzing large, spatially-dependent data sets collected from complex domains with irregular boundaries. Specifically, the awardee, Texas A&M University, will introduce a class of nonstationary models that can flexibly characterize potentially heterogeneous spatial dependence while...
This $399,574 Project Grant awarded by the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to assess and mitigate spatial biases in large-scale mobile location data used for human mobility analysis. The University of Florida, the prime awardee, will undertake four key research tasks: 1) quantify spatial bias in mobile location data; 2) identify causes of spatial biases from the data generation process; 3) develop new methods...
This National Science Foundation Project Grant of $199,999 will support the development of modern spatial and shape analysis methods for heterogeneous high-dimensional geospatial data. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the award will be carried out from July 2022 through June 2025 by the University of Texas at Dallas. Specifically, the Principal Investigator will create three modeling frameworks to analyze heterogeneous geospatial data at different...
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 $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...
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...
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....