U.S.-Netherlands Cooperative Research: Statistical Methods  for Analyzing Data Arising from Reliability Studies         (Mathematical Sciences)
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{'SignBlockName': 'Cassandra Turczak', 'PO_EMAI': None, 'PO_PHON': None}
This is a two-year cooperative research project between Professor               Michael G. Akritas, Department of Statistics, The Pennsylvania State            University, and Professor Willem Albers, Department of Medical                  Informatics and Statistics, University of Limburg, The Netherlands.             The mathematicians are cooperating in a study of tests and estimators           that are based on distance measures, the associated use of the                  bootstrap technique, and the application of the bootstrap to certain            rank procedures including multiple comparisons.  The work may lead to           the development of a common theory for testing and estimation with both         censored and uncensored data, and so is relevant to data arising from           reliability, life testing, and medical studies.  Some of the                    performance aspects that will be considered are: sensitivity to tail            alternatives and to crossing hazards alternatives for the one- and two-         sample problem, respectively, and robustness of the estimators and how          this depends on the degree of censoring.  The second aspect of the work         is concerned with applications of the bootstrap technique to                    approximating the null distribution or the null variance of some common         rank statistics with or without censoring, and to rank-based multiple           comparison procedures with both censored and uncensored data.                                                                                                   This study is concerned with developing and refining statistical                procedures for making "goodness of fit" tests that permit one to judge          how well a chosen model fits data derived from experimental                     observations.  In particular, applications of the procedures will be            made to situations in which data are "censored," that is, missing for           some reason, as when a patient drops out of a clinical study before the         study is over.  The methods would permit a comparison of the                    effectiveness of two or more treatments for a disease, for example, as          well as indicate the levels of confidence that should be placed on the          conclusions that are drawn.  The so-called "bootstrap" technique, a             relatively new and powerful statistical tool, will be applied in this           study to obtain approximations to the true probability distribution of          parameters of interest.  Professor Akritas has considerable experience          in applying statistical methods to censored data.  Professor Albers is          a recognized authority in asymptotic theory with special emphasis in            nonparametric methods.  Collaboration by the two researchers on the             many mathematical and computational aspects of this work should greatly         facilitate the development of the new procedures as well as their               comparison with existing statistical methods.
{'FirstName': 'Michael', 'LastName': 'Akritas', 'PI_MID_INIT': 'G', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Michael G Akritas', 'EmailAddress': 'mga@stat.psu.edu', 'NSF_ID': '000333149', 'StartDate': '10/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Pennsylvania State Univ University Park', 'CityName': 'UNIVERSITY PARK', 'ZipCode': '168021503', 'PhoneNumber': '8148651372', 'StreetAddress': '201 OLD MAIN', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA', 'CONGRESSDISTRICT': '15', 'CONGRESS_DISTRICT_ORG': 'PA15', 'ORG_UEI_NUM': 'NPM2J7MSCF61', 'ORG_LGL_BUS_NAME': 'THE PENNSYLVANIA STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '5914', 'Text': 'WESTERN EUROPE, OTHER'}
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