Mathematical Sciences: A Robust Approach to the Prediction  Problem
{'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}
The research from this proposal will create techniques that             will enhance the way in which scientists predict responses from                 limited amounts of data.  The techniques will be robust in nature               thereby precluding unduly restrictive distributional assumptions.               An approach will be taken similar to what has been done with                    confidence intervals:  robust estimates of location and spread                  will be used to create prediction intervals for a function of a                 set of future observations.  Recent advances in the field of                    adaptive estimation will also be used to classify the underlying                distribution.                                                                           This research will go far beyond currently available                    statistical methods for predicting responses.  Statistical                      prediction will be developed for new types of data, types for                   which existing statistical methods have been used even though                   scientists knew they were misfit because no other methods                       existed.  Since the ability to predict is vital to scientists and               engineers, the results from this research will aid in advancing                 the state-of-the-art in a variety of fields.
{'FirstName': 'Paul', 'LastName': 'Horn', 'PI_MID_INIT': 'S', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Paul S Horn', 'EmailAddress': 'paul.horn@uc.edu', 'NSF_ID': '000196771', 'StartDate': '07/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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{'Code': '126900', 'Text': 'STATISTICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8706044.xml'}