Dimension reduction in spatial regression with kernel SAVE method
M\`etolidji Moquilas Raymond Affossogbe, Guy Martial Nkiet, and Carlos, Ogouyandjou
TL;DR
This paper introduces a kernel-based approach to dimension reduction in spatial regression, extending SAVE to spatially dependent data and proving the consistency of the estimators.
Contribution
It proposes a kernel estimator for the interest matrix and EDR space in spatial data, addressing dependence and establishing theoretical consistency.
Findings
Kernel estimators are consistent for spatial data.
Extension of SAVE to spatially dependent observations.
Theoretical validation of the proposed method.
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.
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Taxonomy
TopicsSpatial and Panel Data Analysis · Statistical Methods and Inference · Soil Geostatistics and Mapping