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Hierarchical adaptive regression kernels for regression with functional predictors

Author
Woodard, Dawn B.; Crainiceanu, Ciprian; Ruppert, David
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.
Date Issued
2011-01-11Subject
Functional data analysis
Type
article