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• W2941239653 abstract "A new class of models for non-Gaussian spatial random fields is developed for spatial field reconstruction in environmental and sensory network monitoring. The developed family of models utilises a class of transformation functions known as the Tukey g-and-h transformation to create a new class of warped spatial Gaussian process model which can support various desirable features such as flexible marginal distributions, which can be skewed and/or heavy-tailed. The resulting model is widely applicable for a wide range of spatial field reconstruction applications. To utilise the model for such applications in practice, we first need to derive the statistical properties of the new family of Tukey g-and-h random fields. We are then able to derive five different objectives to perform spatial field reconstruction. These include the Minimum Mean Squared Error (MMSE), Maximum A-Posteirori (MAP) and the Spatial-Best Linear Unbiased (S-BLUE) estimators as well as the Spatial Regional and Level Exceedance estimators. Extensive simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields as is classically utilised." @default.
• W2941239653 created "2019-05-03" @default.
• W2941239653 creator A5051994726 @default.
• W2941239653 creator A5087616647 @default.
• W2941239653 creator A5091256297 @default.
• W2941239653 date "2018-01-01" @default.
• W2941239653 modified "2023-09-23" @default.
• W2941239653 title "Spatial Field Reconstruction of Non-Gaussian Random Fields: The Tukey G-and-H Random Process" @default.
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• W2941239653 doi "https://doi.org/10.2139/ssrn.3159687" @default.
• W2941239653 hasPublicationYear "2018" @default.
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