Functional robust regression for longitudinal data
Methodology
2012-12-03 v1
Abstract
We present a robust regression estimator for longitudinal data, which is especially suited for functional data that has been observed on sparse or irregular time grids. We show by simulation that the proposed estimators possess good outlier-resistance properties compared with the traditional functional least-squares estimator. As an example of application, we study the relationship between levels of oxides of nitrogen and ozone in the city of San Francisco.
Cite
@article{arxiv.1211.7332,
title = {Functional robust regression for longitudinal data},
author = {Daniel Gervini},
journal= {arXiv preprint arXiv:1211.7332},
year = {2012}
}