Response envelopes for linear coregionalization models

Paul May, Matthew Biesecker and Hossein Moradi Rekabdarkolaee

Journal of Multivariate Analysis, 2022, vol. 192, issue C

Abstract: Dimension reduction provides a useful tool for statistical data analysis with high-dimensional data. In this paper, we develop a parsimonious multivariate spatial regression model with a non-separable covariance function. The efficacy of this new solution is illustrated through simulation studies and a real data analysis. We show that for cases where the marginal spatial correlations are different from each other, the proposed non-separable model provides better estimation and inference than the related separable model, and provides tighter inference than a non-separable spatial model without dimension reduction when there is immaterial variation in the data.

Keywords: Dimension reduction; Envelope model; Multivariate response; Reducing subspace; Spatial correlation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.jmva.2022.105015

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