Analysis of Covariance Operators.
fdcov provides a variety of tools for the analysis of
This package contains a collection of tools for performing
statistical inference on functional data specifically through
an analysis of the covariance structure of the data. It includes
two methods for performing a k-sample test for equality
of covariance in
For supervised and unsupervised learning,
it contains a method to classify functional data with
respect to each category's covariance operator in
and it contains a method to cluster functional data,
again based on the covariance structure of the data.
The current version of this package assumes that all functional
data is sampled on the same grid at the same intervals. Future
updates are planned to allow for the below methods to interface
fda package and its functional basis representations
of the data.
Kashlak, Adam B, John AD Aston, and Richard Nickl (2016). "Inference on covariance operators via concentration inequalities: k-sample tests, classification, and clustering via Rademacher complexities", April, 2016 (in review)
Pigoli, Davide, John AD Aston, Ian L Dryden, and Piercesare Secchi. "Distances and inference for covariance operators." Biometrika (2014): asu008.