Modeling spatial data using local likelihood estimation and a Matérn to spatial autoregressive translation

  title={Modeling spatial data using local likelihood estimation and a Mat{\'e}rn to spatial autoregressive translation},
  author={A. Wiens and D. Nychka and W. Kleiber},
  journal={arXiv: Methodology},
Modeling data with non-stationary covariance structure is important to represent heterogeneity in geophysical and other environmental spatial processes. In this work, we investigate a multistage approach to modeling non-stationary covariances that is efficient for large data sets. First, we use likelihood estimation in local, moving windows to infer spatially varying covariance parameters. These surfaces of covariance parameters can then be encoded into a global covariance model specifying the… Expand

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