This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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499
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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 Nonparametric Statistics
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Abstract - #308385 |
Title:
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Structured Penalties for Generalized Functional Linear Models (GFLM)
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Author(s):
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Jaroslaw Harezlak*+ and Tim Randolph and Ziding Feng
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Companies:
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Indiana University School of Medicine and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
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Address:
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410 W 10th St., Suite 3000, Indianapolis, IN, 46202,
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Keywords:
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Functional data analysis ;
GLM ;
joint spectral decomposition ;
regularization ;
penalized regression
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Abstract:
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GFLMs are often used to estimate the relationship between a predictor function and a response. Approaches used either reduce the functions by estimating their principal components or project the functions onto the span of fixed bases. A major challenge in GFLM is to incorporate the structural properties of the functions into the analysis. This presentation provides an extension of a recently proposed method - PEER (partially empirical eigenvectors for regression) for FLM to GLFM. The PEER approach to FLMs incorporates the structure of the functions via a joint spectral decomposition of the predictor functions and a penalty operator into the estimation process via a generalized singular value decomposition. We extend this approach to GFLMs and compare the estimation performance with the classical methods. Finally, we apply our methodology to a mass spectrometry data with binary outcomes.
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