Testing Independence Between Two Spatial Random Fields
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DOI: 10.1007/s13253-020-00421-3
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- Johnstone, Iain M. & Lu, Arthur Yu, 2009. "On Consistency and Sparsity for Principal Components Analysis in High Dimensions," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 682-693.
- Leo Breiman & Jerome H. Friedman, 1997. "Predicting Multivariate Responses in Multiple Linear Regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 3-54.
- Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
- Joshua Hewitt & Jennifer A. Hoeting & James M. Done & Erin Towler, 2018. "Remote effects spatial process models for modeling teleconnections," Environmetrics, John Wiley & Sons, Ltd., vol. 29(8), December.
- Wen‐Ting Wang & Hsin‐Cheng Huang, 2018. "Regularized spatial maximum covariance analysis," Environmetrics, John Wiley & Sons, Ltd., vol. 29(2), March.
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- Matsui, Muneya & Mikosch, Thomas & Roozegar, Rasool & Tafakori, Laleh, 2022. "Distance covariance for random fields," Stochastic Processes and their Applications, Elsevier, vol. 150(C), pages 280-322.
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Keywords
Canonical correlation analysis; Dimension reduction; High-dimensional test; Irregularly spaced data; Multiresolution spline basis functions; Teleconnection; Tracy–Widom distribution;All these keywords.
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