Computational Statistics: Structural Inference for Large, High Dimensional Data Sets
{'Code': '03040109', 'Directorate': {'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'LongName': 'Division Of Mathematical Sciences'}}
{'SignBlockName': 'Alvin I. Thaler'}
This project will explore a variety of mathematical and statistical theories related to large, high dimensional data sets. Specifically the focus is on graphical methods for data representation in higher dimensions, structural inference, density and function estimation, elements of computational geometry, the relationship of algorithms and architectures, and high - interaction graphics. All of these topics intertwine to form the conceptual core of the approach to large, high dimensional data sets.
{'FirstName': 'Edward', 'LastName': 'Wegman', 'EmailAddress': 'ewegman@gmu.edu', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'George Mason University', 'CityName': 'FAIRFAX', 'ZipCode': '220304422', 'PhoneNumber': '7039932295', 'StreetAddress': '4400 UNIVERSITY DR', 'CountryName': 'United States', 'StateName': 'Virginia', 'StateCode': 'VA'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8710931.xml'}