Project Grant 2120318

This Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program, with a total funding of $299,996, aims to develop software for post-linkage data analysis. The project builds on record linkage methods to address potential errors and uncertainty in data analysis performed after merging multiple data sources. The software will be developed in popular data science programming languages and tailored to the needs of federal...

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 $440,000 National Science Foundation project grant under the Mathematical and Physical Sciences program (CFDA 47.049) will support the development of modal regression models for abnormal data analysis. The principal investigator at the University of California, Riverside will develop a suite of new parametric and nonparametric modal regression techniques as an alternative to traditional mean and quantile regression models. This involves imposing assumptions on the conditional mode of a...

This National Science Foundation Project Grant award under the Social, Behavioral, and Economic Sciences program (CFDA 47.075) provides $349,984 to The Washington University in University City, Missouri to develop innovative computational and statistical methods for quantile regression analysis of big data. The key products and services to be delivered through this 3-year award include: An efficient online framework for quantile regression analysis of data streams to enable cost-effective,...

This National Science Foundation Project Grant of $399,322 awarded September 1, 2022 through August 31, 2026 under the Mathematical and Physical Sciences program (CFDA 47.049) will fund the development of leading-edge statistical methods for unsupervised and semi-supervised heterogeneity analysis based on Gaussian graphical models. The awardee, Yale University, will systematically develop Gaussian graphical model-based approaches to examine complicated scenarios involving latent and regulating...

This Project Grant from the National Science Foundation's Social, Behavioral, and Economic Sciences program will fund $291,152 over three years to develop new statistical and econometric methods for analyzing social network data. Northwestern University will use the funding to build new nonparametric regression frameworks and investigate the informational content of partial network data generated through referral sampling and contingency tables. The researchers will characterize the...

This $193,038 five-year Project Grant award from the National Science Foundation's Social, Behavioral, and Economic Sciences (CFDA 47.075) program will support research at Colorado State University to develop flexible Bayesian record linkage models. The goal is to enable more accurate linking of multiple data sets without unique identifiers, which is crucial for leveraging diverse data sources to address complex problems. The research will focus on addressing key limitations in current record...

This $279,983 National Science Foundation project grant supports research at Brown University to develop hybrid statistical and econometric modeling methods. Funded under the NSF Social, Behavioral, and Economic Sciences program, the three-year award beginning August 2022 aims to advance modeling approaches that account for imperfect data measurement and the reality that models approximate rather than perfectly represent the world. The research will modify method-of-moments techniques to...

This three-year, $674,542 National Science Foundation project grant supports research at the University of California Santa Cruz to develop Bayesian statistical and machine learning methods for analyzing complex survey data from the federal statistical system. The grant falls under the NSF's Social, Behavioral, and Economic Sciences program (CFDA 47.075), which promotes basic research and education in these fields. Specifically, the investigators will extend existing models using data...

This $213,462 National Science Foundation award under the Mathematical and Physical Sciences program will support the development of new statistical methods for analyzing functional medical data with skewness and outliers. Specifically, the University of North Carolina at Charlotte will develop dimension reduction techniques for quantile regression to analyze functional magnetic resonance imaging and electroencephalogram data related to attention deficit hyperactivity disorder and alcoholism....