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Dimension reduction in spatial regression with kernel SAVE method

Published 22 Sep 2019 in math.ST and stat.TH | (1909.09996v1)

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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