The National Science Foundation (NSF) awarded a $132,370 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to George Washington University. The grant, titled "Collaborative Research: Unlocking Complex Heterogeneity in Large Spatial-Temporal Data with Scalable Quantile Learning," aims to develop scalable and efficient quantile learning techniques to analyze large-scale, heterogeneous spatial-temporal data. The research seeks...
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...
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...
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 $309,960 federal Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will fund a research project at the University of Chicago to improve the methodology for testing coefficients in linear regressions. The project will develop new test methods that are robust to assumptions about error term distributions and sample size, allowing for more precise coefficient estimates. This research aims to enhance decision-making,...
This $233,955 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will support a research project at The Washington University in St. Louis to develop improved methods for probabilistic data integration and record linkage without the use of unique identifiers. The central objectives are to create computationally efficient and accurate techniques for merging large datasets from multiple sources, which is a critical...
This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) in the amount of $197,007 is supporting collaborative research to develop new statistical theories and methodologies for tackling issues related to false discovery rate control in regression analysis. The research aims to provide novel statistical tools for analyzing complex data from diverse scientific domains such as brain imaging, genome-wide association studies, and atmospheric...
This $285,000 federal Project Grant award from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) will fund research to develop new statistical methods to guide economic and public policy decisions in rapidly changing environments. The research aims to build on recent advances in statistical decision theory, causal inference, and machine learning to create econometric models that can effectively inform evidence-based policymaking while...
This $131,615 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences, under the CFDA program 47.049 Mathematical and Physical Sciences, aims to develop novel Bayesian statistical models for analyzing complex high-dimensional health data. The research will focus on creating improved joint models that can leverage information from longitudinal measurements, such as clinical data and biomarkers, to better predict time-to-event outcomes like disease...
This $350,000 federal Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will support the development of new theory and methods for assessing the sensitivity of causal inferences to violations of underlying assumptions in quasi-experimental research designs. The research project aims to create a comprehensive suite of sensitivity analysis tools for popular causal inference techniques such as instrumental variables,...