# Sliced Inverse Regression for Spatial Data

@article{Muehlmann2020SlicedIR,
title={Sliced Inverse Regression for Spatial Data},
author={Christoph Muehlmann and Hannu Oja and Klaus Nordhausen},
journal={arXiv: Methodology},
year={2020}
}
• Published 2 July 2020
• Mathematics
• arXiv: Methodology
Sliced inverse regression is one of the most popular sufficient dimension reduction methods. Originally, it was designed for independent and identically distributed data and recently extend to the case of serially and spatially dependent data. In this work we extend it to the case of spatially dependent data where the response might depend also on neighbouring covariates when the observations are taken on a grid-like structure as it is often the case in econometric spatial regression…
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