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Activity Number:
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535
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Type:
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Invited
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #302856 |
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Title:
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Model Selection for Partial Smoothing Splines
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Author(s):
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Hao (Helen) Zhang*+ and Guang Cheng
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Companies:
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North Carolina State University and Purdue University
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Address:
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Campus Box 8203, Raleigh, NC, 27695,
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Keywords:
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partial splines ; variable selection ; solution path ; nonparametric ; oracle ; sparse
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Abstract:
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We introduce a unified approach for simultaneous variable selection and model estimation in partial smoothing spline models. Theoretical properties of the estimators are studied. Firstly, under general regularity conditions we show that the estimator for the parametric components is consistent and asymptotically normal, and even more interestingly, the nonparametric function estimator is shown to be able to achieve the optimal nonparametric rate at the same time. Secondly, when tuning parameters are properly chosen, the procedure has the desired oracle properties for variable selection. Another advantage of the new procedure is its easiness for computation. We suggest a path-finding type algorithm to compute the estimators. Simulated and real examples are used to illustrate performance of the approach.
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