This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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215
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Type:
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Invited
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Date/Time:
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Monday, August 2, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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Abstract - #306161 |
Title:
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Prediction via Sparse Kernel PCA
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Author(s):
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Tianxi Cai*+
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Companies:
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Harvard School of Public Health
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Address:
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655 Huntington Avenue, 411, Boston, 02115, United States
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Keywords:
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kernel machine ;
risk prediction ;
principal component analysis ;
shrinkage methods
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Abstract:
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Accurate risk assessment can have a great impact in public health. The standard approach to constructing risk prediction rules is to assume a linear effect and fit models such as the GLM. However, when markers relate to the phenotype simultaneously via a complex structure, prediction rules based on such linearity assumptions may not be effective. To overcome such difficulties, we employ a kernel machine regression framework and estimate prediction rules that incorporate potential non-linear and interactive effects of a set of markers, such as genes within a pathway. To achieve an optimal trade-off between the complexity of the model and the variability in the estimation, we consider sparse kernel principal component regression that leads to a reduced effective degree of freedom. Simulation studies suggest that the proposed procedures work well in finite sample.
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Authors who are presenting talks have a * after their name.
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