Journal article
Approximating fragmented functional data by segments of Markov chains
A Delaigle, P Hall
Biometrika | OXFORD UNIV PRESS | Published : 2016
Abstract
We consider curve extension and linear prediction for functional data observed only on a part of their domain, in the form of fragments. We suggest an approach based on a combination of Markov chains and nonparametric smoothing techniques, which enables us to extend the observed fragments and construct approximated prediction intervals around them, construct mean and covariance function estimators, and derive a linear predictor. The procedure is illustrated on real and simulated data.
Grants
Funding Acknowledgements
We downloaded the bone data from http://fedc.wiwi.hu-berlin.de/quantnet/index.php?p=show&id=896. This research was supported by the Australian Research Council. Delaigle is and Hall was a member of the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers. Peter passed away on 9 January 2016 in Melbourne, Australia. He was a wonderful man and an extraordinary researcher. He will be sorely missed.