Mathematical Sciences: Assessment of Sequential             Probabilistic Forecasting Procedures
{'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 investigator considers the problem of predicting the                   next member of a sequence of random variables by using previous                 values of the sequence and possibly other information.  The                     forecast takes the form of a probability distribution on the                    possible values of the random variable.  (The true distribution                 of the random variable is unknown.)  Assessment criteria or                     "scoring rules" which judge the "calibration" or accuracy of the                forecast are to be examined.  The scoring rules will be examined                under the assumption that the forecast distribution is correct as               well as under the assumption that it is incorrect in certain                    aspects.  The investigator will also consider these problems for                a forecast region or "prediction region" rather than a complete                 forecast probability distribution.  (By a prediction region we                  mean the interval from 65 to 73 in a forecast such as : "There is               a 90% chance that the temperature will be between 65 degrees and                73 degrees.") The  problems involve using distance measures                     between distributions. Continguity arguments and direct                         martingale arguments will be needed.                                                 The investigator's work involves assessing sequential                      forecasting procedures that use probabilty.  Such procedures                    occur in the following examples:  a meteorologist gives a                       probability that it will rain the next day; medical clinicians                  (or expert systems) give subjective probabilities that particular               patients are suffering from specific diseases; a computer                       specialist predicts the distribution of future failure times of                 software or hardware based on past experience.  Sequential                      feedback is essential in the process in the sense that as the                   outcome for previous forecasts becomes known to the forecaster                  this and other information is incorporated into the next                        forecast. The long term behavior of the  forecasting procedure                  will also be examined.
{'FirstName': 'Francoise', 'LastName': 'Seillier-Moiseiwitsch', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Francoise Seillier-Moiseiwitsch', 'EmailAddress': 'seillier@georgetown.edu', 'NSF_ID': '000448080', 'StartDate': '01/01/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of California-Berkeley', 'CityName': 'BERKELEY', 'ZipCode': '947101749', 'PhoneNumber': '5106433891', 'StreetAddress': '1608 4TH ST STE 201', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_ORG': 'CA12', 'ORG_UEI_NUM': 'GS3YEVSS12N6', 'ORG_LGL_BUS_NAME': 'REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '1269', 'Text': 'STATISTICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8814094.xml'}