Mathematical Sciences: Applied Multivariate Tools
{'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 encompasses five topics aimed at the                         development of better working tools for the analysis of                         multivariate data:  (1)  Procedures for subset selection and                    evaluation in regression, (2) A new and better procedure for                    fitting nonlinear-nonparametric additive models through                         transformations of the predicted and the predictor variables, (3)               Global nonlinear optimization methods, (4) Prototype analysis                   algorithms, and (5) Nonlinear smoothing methods.                                     This research is in the statistical area of regression.                    Data analytic tools are extended following a significant step                   beyond traditional general linear models that was made                          practically feasible with the availability of cheap computational               horsepower.
{'FirstName': 'Leo', 'LastName': 'Breiman', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Leo Breiman', 'EmailAddress': 'leo@stat.Berkeley.EDU', 'NSF_ID': '000460457', 'StartDate': '05/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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