This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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576
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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SSC
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| Abstract - #306756 |
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Title:
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Penalized Functional Regression
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Author(s):
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Jeff Goldsmith*+ and Jennifer Feder and Ciprian Crainiceanu and Brian Scott Caffo and Daniel Reich
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Companies:
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Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and The Johns Hopkins University and Johns Hopkins Bloomberg School of Public Health and National Institutes of Health
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Address:
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, , ,
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Keywords:
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Functional Regression ;
Smoothing
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Abstract:
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We develop fast fitting methods for generalized functional linear models. An undersmooth of the functional predictor is obtained by projecting on a large number of smooth eigenvectors and the coefficient function is estimated using penalized spline regression. Our method can be applied to many functional data designs including functions measured with and without error, sparsely or densely sampled. The methods also extend to the case of multiple functional predictors or functional predictors with a natural multilevel structure. Our approach can be implemented using standard mixed effects software and is computationally fast.
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