This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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505
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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 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #306403 |
Title:
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Automatic Smoothing Parameter Selection for Nonparametric Function Estimation with Clustered and Longitudinal Data
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Author(s):
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Jianhua Huang+ and Ganggang Xu*
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Companies:
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Texas A&M University and Texas A&M University
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Address:
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447 Blocker Building, College Station, TX, 77843-3143,
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Keywords:
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smoothing ;
nonparametric function estimation
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Abstract:
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While automatic smoothing parameter selection for nonparametric function estimation has been extensively researched for independent data, it is much less so for clustered and longitudinal data. Although leave-cluster(subject)-out cross-validation (CV) has been widely used, its theoretical property is unknown and its minimization is computationally expensive, especially when there are multiple smoothing parameters. In this paper, we show that leave cluster(subject)-out CV is optimal in that its minimization is asymptotically equivalent to the minimization of the empirical loss function. We develop an efficient algorithm to compute the smoothing parameters that minimize the CV criterion. Furthermore, we derive two simplifications of the leave-cluster(subject)-out CV, which are used to develop even more efficient algorithms for selecting the smoothing parameters.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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