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Spatial independent component analysis for heteroskedastic random fields

Author

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  • Morales Martínez, Rodrigo
  • Nordhausen, Klaus
  • Ruiz, Anne M.

Abstract

Independent component analysis (ICA) is a widely used method for uncovering statistically independent and non-Gaussian latent signals in high-dimensional data. In this paper, we propose novel extensions for classical Fourth Order Blind Identification (FOBI) and Joint Approximate Diagonalization of Eigenmatrices (JADE) estimators to the spatial domain, introducing spFOBI and spJADE. Our approaches are designed to recover independent components in heteroskedastic random fields by exploiting the spatial structure of fourth-order cumulants. We perform simulations to demonstrate that spFOBI and spJADE recover latent structures in the presence of spatial heteroskedasticity and volatility clustering, scenarios in which the current covariance based methods fail. We also show how the methods outperform their classical counterparts, which do not account for spatial dependence.

Suggested Citation

  • Morales Martínez, Rodrigo & Nordhausen, Klaus & Ruiz, Anne M., 2026. "Spatial independent component analysis for heteroskedastic random fields," Statistics & Probability Letters, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:stapro:v:238:y:2026:i:c:s0167715226002221
    DOI: 10.1016/j.spl.2026.110858
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