Mathematical Sciences: Model Robust Design and Inference
{'Code': '03040107', 'Directorate': {'Abbreviation': 'MPS', 'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'Abbreviation': 'DMS', 'LongName': 'Division Of Mathematical Sciences'}}
{'SignBlockName': 'Alan Izenman', 'PO_EMAI': None, 'PO_PHON': None}
This research will be on experimental designs in settings               where systematic departures from exact linear models are allowed                but controlled.  The study of design problems in dimensions                     higher than one is facilitated greatly by controlling the                       departure through the use of a stochastic process prior.                        Problems with and without measurement error are of interest.                    Important and interesting problems fitting the latter case arise                in numerical integration and in the selection of inputs to                      computer model experiments and simulations.  Algorithms for                     computing designs are to be devised, studied, and compared.                     Prediction of linear designs are to be devised, studied and                     compared.  Prediction of linear functionals of the underlying                   regression or process will be emphasized; non-linear functionals                bring added concerns also requiring study.  The complexity of the               problems and the necessity for addressing complex design spaces                 leads to the need and use of advanced computational resources.                          The primary goal of statistical experimental design is to               assess the feasibility of a proposed experiment and to optimize                 the use of resources in the experiment.  Most of current                        statistical theory applies to very simply configured experiments                and analysis techniques.  This is so because, until the computer                revolution, the feasibility of experiments were severely limited                by data collection and computational capabilities.  This research               is to broaden the theory of experimental design, to realistically               apply to more modern scientific settings where technological                    advances are permitting more complex questions to be studied in                 controlled and controlled experiments.
{'FirstName': 'Jerome', 'LastName': 'Sacks', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Jerome Sacks', 'EmailAddress': 'sacks@niss.org', 'PI_PHON': '9196859300', 'NSF_ID': '000139736', 'StartDate': '06/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Illinois at Urbana-Champaign', 'CityName': 'URBANA', 'ZipCode': '618013620', 'PhoneNumber': '2173332187', 'StreetAddress': '506 S WRIGHT ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Illinois', 'StateCode': 'IL', 'CONGRESSDISTRICT': '13', 'CONGRESS_DISTRICT_ORG': 'IL13', 'ORG_UEI_NUM': 'Y8CWNJRCNN91', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF ILLINOIS', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '1269', 'Text': 'STATISTICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8703802.xml'}