Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 22, Issue 4, Pages 777-800Publisher
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2012.694765
Keywords
Electroencephalogram; Functional data analysis; Functional linear model; Kernel mixture; Levy adaptive regression kernels; Nonparametric Bayes
Categories
Funding
- National Institute of Neurological Disorders and Stroke [R01NS060910]
- National Science Foundation [CMMI-0926814]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1209103] Funding Source: National Science Foundation
Ask authors/readers for more resources
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 stochastically across subjects. We propose 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, to quantify the association between sleep characteristics and health outcomes. Software and technical appendices are provided in the online supplementary materials.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available