Mathematical Sciences: Multivariable Regular and ExponentialVariation with Application to Multiple Regression and to    Convolutions of Multivariate Distributions
{'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}
This research will seek results and their applications of                  multivariate regular variation.  The applications will be to                    least square estimators in multiple regression with random                      predictors.  The notable part will be that the assumptions on the               underlying distributions will be minimal, allowing for infinite                 moments and disparity among distributional types.  Basic among                  problems of study is the regular variation of probability tails                 for product of a random vector with an independent scaler and for               the matrix of cross products formed from a random vector.  A                    related problem to be studied will involve the exponential tail                 behavior of convolutions.  Extensions of the concepts of                        subexponentiality and convolution equivalency will be sought.                   The research will have applications for multivariate infinitely                 divisible laws for compound multivariate distributions.
{'FirstName': 'Daren', 'LastName': 'Cline', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Daren B Cline', 'EmailAddress': 'dcline@stat.tamu.edu', 'NSF_ID': '000432650', 'StartDate': '09/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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{'Code': '126900', 'Text': 'STATISTICS'}
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