Mathematical Sciences: Conditional and Unconditional Confidence
{'Code': '03040107', 'Directorate': {'LongName': 'Direct For Mathematical & Physical Scien'}, 'Division': {'LongName': 'Division Of Mathematical Sciences'}}
{'SignBlockName': 'Alan Izenman'}
The research investigates reconciliation of unconditional (pre-data) inference and conditional (post-data) inference using an objective means of assessing conditional validity of frequentist confidence. General methods of assigning post-data confidence are considered which are based on posterior distributions. The resulting properties and frequentist validity of the post-data confidence estimator are examined. Post-data inference for volume-reducing confidence sets for normal means and variances is to be studied where it is possible to achieve lower volume and coherent post-data confidence in a frequency- valid way. The conditional properties of admissible nonrandomized confidence sets from discrete distributions will be investigated with the goal of obtaining coherent frequency-valid post-data inference.
{'FirstName': 'George', 'LastName': 'Casella', 'EmailAddress': 'casella@stat.ufl.edu', 'StartDate': '07/01/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Cornell Univ - State: AWDS MADE PRIOR MAY 2010', 'CityName': 'Ithica', 'ZipCode': '148502488', 'PhoneNumber': '6072555014', 'StreetAddress': '373 Pine Tree Road', 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1989&All=true', 'xml': '8900369.xml'}