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Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/21991
Title: Hierarchical adaptive regression kernels for regression with functional predictors
Authors: Woodard, Dawn B.
Crainiceanu, Ciprian
Ruppert, David
Keywords: Functional data analysis
Issue Date: 11-Jan-2011
Abstract: We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies across subjects. We propose full Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for regression with functional predictors, and show that our method is more effective and efficient for data that include features occurring at varying locations. We apply our methodology to a large and complex dataset from the Sleep Heart Health Study, in order to better understand the relationship between sleep characteristics and health outcomes.
URI: http://hdl.handle.net/1813/21991
Appears in Collections:ORIE Technical Reports

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