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

@article{Murakami2021CompositionallywarpedAM,
  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},
  year={2021},
  volume={43},
  pages={100520}
}
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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References

SHOWING 1-10 OF 76 REFERENCES
A memory-free spatial additive mixed modeling for big spatial data
TLDR
This study develops a spatial additive mixed modeling approach estimating spatial and non-spatial effects from large samples, such as millions of observations, with a Moran coefficient-based approach and applies it to an income analysis using United States (US) data in 2015.
Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data
TLDR
A Vecchia-Laplace approximation is proposed for GGPs, which combines a Laplace approximation to the non-Gaussian likelihood with a computationally efficient VecChia approximation toThe GP, resulting in a simple, general, scalable, and accurate methodology.
Deep Compositional Spatial Models
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena. Such processes may often be
Non-Gaussian spatiotemporal modelling through scale mixing
We construct non-Gaussian processes that vary continuously in space and time with nonseparable covariance functions. Starting from a general and flexible way of constructing valid nonseparable
Compositionally-Warped Gaussian Processes
TLDR
The compositionally-warped GP (CWGP) is proposed, a non-Gaussian generative model whose expressiveness follows from its deep compositional architecture, and its computational efficiency is guaranteed by the analytical inverse warping.
Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis
TLDR
A fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients, that is useful not only for selecting factors influencing crime risk but also for predicting crime events.
Multivariate transformed Gaussian processes
We set up a general framework for modeling non-Gaussian multivariate stochastic processes by transforming underlying multivariate Gaussian processes. This general framework includes multivariate
Spatially varying coefficient modeling for large datasets: Eliminating N from spatial regressions
Abstract While spatially varying coefficient (SVC) modeling is popular in applied science, its computational burden is substantial. This is especially true if a multiscale property of SVC is
Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm
TLDR
The proposed algorithm can be seen as a generalization of the algorithm by Schall (1991)—for variance components estimation—to deal with non-standard structures of the covariance matrix of the random effects.
A Generalized Linear Model Approach to Spatial Data Analysis and Prediction
TLDR
This article demonstrates how the theory of generalized linear models and quasi-likelihood can be extended to include the analysis of discrete and categorical spatial data and provides a flexible method for spatial prediction using non-normal data.
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