Project Grant 1853099

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 $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 three-year National Science Foundation project grant of $201,690 will fund research at Princeton University to develop new methods for analyzing strongly correlated economic data across geographic space. The research will establish a formal framework for studying spatial unit root processes similar to time series analysis, derive tests to detect strong spatial correlation, and suggest methods to allow for valid statistical inference from spatially correlated data. Specific projects will...

The National Science Foundation (NSF) awarded a 3-year, $250,015 Project Grant under its Mathematical and Physical Sciences program (CFDA 47.049) to The Trustees of Columbia University in the City of New York. The primary goal of this collaborative research project is to develop advanced statistical and machine learning techniques to forecast and analyze high-dimensional extreme events, such as extreme climate conditions and social phenomena. Key research thrusts include: 1) Learning the...

The National Science Foundation (NSF) Social, Behavioral, and Economic Sciences program awarded a $214,625 Project Grant to the San Diego State University Research Foundation (SDSURF) to develop new model-based estimators for improving the precision of population parameter estimates in small area survey sampling. The project will create clustered coefficient regression models that can better handle heterogeneous relationships between survey variables and auxiliary data, compared to traditional...

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 Project Grant from the National Science Foundation's National Center for Science and Engineering Statistics will fund the development of Bayesian statistical and machine learning methodologies tailored for complex survey and census data. Awarded $743,050 under the Social, Behavioral, and Economic Sciences program, the grant will support research at the University of Missouri from September 2022 through August 2025. The research aims to advance computational efficiency and expand...

This National Science Foundation (NSF) Division of Mathematical Sciences Project Grant, titled "COLLABORATIVE RESEARCH: LEARNING AND FORECASTING HIGH-DIMENSIONAL EXTREMES: SPARSITY, CAUSALITY, PRIVACY," aims to develop new statistical methods for forecasting extreme events and assessing their impacts. The $200,000 award to Cornell University, valid from August 15, 2023 to July 31, 2026, seeks to address challenges in analyzing high-dimensional, contaminated data to extract key features...

This Project Grant award of $200,000.00 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) aims to develop efficient and accurate statistical methodology that can be applied in practice without strong assumptions about data. The research project, titled "Collaborative Research: Resampling Bridges for Complex Data," will focus on producing "bridged" resampling methods for complex data by connecting and combining separate...

This Project Grant award from the National Science Foundation's (NSF) Division of Mathematical Sciences under the CFDA 47.049 Mathematical and Physical Sciences program provides $115,000 to West Virginia University Research Corporation to develop innovative methodologies for analyzing and quantifying extremal patterns in functional data, such as daily precipitation curves, pollution levels, and intraday stock returns. The 3-year project aims to create a novel "Extremal Correlation...