This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 215
Type: Invited
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #306161
Title: Prediction via Sparse Kernel PCA
Author(s): Tianxi Cai*+
Companies: Harvard School of Public Health
Address: 655 Huntington Avenue, 411, Boston, 02115, United States
Keywords: kernel machine ; risk prediction ; principal component analysis ; shrinkage methods

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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