Dimension reduction in spatial regression with kernel SAVE method
Statistics Theory
2019-09-24 v1 Statistics Theory
Abstract
We consider the smoothed version of sliced average variance estimation (SAVE) dimension reduction method for dealing with spatially dependent data that are observations of a strongly mixing random field. We propose kernel estimators for the interest matrix and the effective dimension reduction (EDR) space, and show their consistency.
Keywords
Cite
@article{arxiv.1909.09996,
title = {Dimension reduction in spatial regression with kernel SAVE method},
author = {Mètolidji Moquilas Raymond Affossogbe and Guy Martial Nkiet and Carlos Ogouyandjou},
journal= {arXiv preprint arXiv:1909.09996},
year = {2019}
}