Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data

  title={Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data},
  author={Daisuke Murakami and Mami Kajita and Seiji Kajita and Tomoko Matsui},
  journal={spatial statistics},
Abstract As with the advancement of geographical information systems, non-Gaussian spatial data sets are getting larger and more diverse. This study develops a general framework for fast and flexible non-Gaussian regression, especially for spatial/spatiotemporal modeling. The developed model, termed the compositionally-warped additive mixed model (CAMM), combines an additive mixed model (AMM) and the compositionally-warped Gaussian process to model a wide variety of non-Gaussian continuous data… 
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