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Response envelopes for linear coregionalization models

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Listed:
  • May, Paul
  • Biesecker, Matthew
  • Rekabdarkolaee, Hossein Moradi

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.

Suggested Citation

  • May, Paul & Biesecker, Matthew & Rekabdarkolaee, Hossein Moradi, 2022. "Response envelopes for linear coregionalization models," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:jmvana:v:192:y:2022:i:c:s0047259x22000410
    DOI: 10.1016/j.jmva.2022.105015
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    References listed on IDEAS

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