Mathematical Sciences: Robust Recursive Procedures and      Computationally Efficient Detection of Model Failures
{'Code': '03040107', 'Directorate': {'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'LongName': 'Division Of Mathematical Sciences'}}
{'SignBlockName': 'Alan Izenman'}
This research will explore a broad class of recursive robust               estimation procedures.  Recursive procedures are important when                 it is too expensive to store all of the previous data and when                  real-time estimates are required.  Update procedures can provide                good approximate estimates at lower costs.  The particular                      problem to be investigated relates to abrupt changes in                         parameters of static or dynamic models that arise in many areas.                This type of problem is called jump, failure, or fault detection                in engineering and in quality control, and as detection of model                instability or change point analysis in statistics and                          econometrics.  Much of the past work assumes an underlying                      Gaussian distribution.  The principal investigator will explore                 more resistent recursive procedures.
[{'FirstName': 'Roy', 'LastName': 'Welsch', 'EmailAddress': None, 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Alexander', 'LastName': 'Samarov', 'EmailAddress': 'samarov@mit.edu', 'StartDate': '08/01/1987', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]
{'Name': 'Massachusetts Institute of Technology', 'CityName': 'Cambridge', 'ZipCode': '021394301', 'PhoneNumber': '6172531000', 'StreetAddress': '77 MASSACHUSETTS AVE', 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA'}
{'Code': '1269', 'Text': 'STATISTICS'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8706393.xml'}