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Nearest-Neighbor Mixture Models for Non-Gaussian Spatial Processes

X Zheng, A Kottas, B Sansó - arXiv preprint arXiv:2107.07736, 2021 - arxiv.org
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… Thus, it facilitates efficient, full simulation-based inference. We study model construction and properties analytically through specification of bivariate distributions that define the local …

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BibTeX

@misc{zheng2022nearestneighbor,
title={Nearest-Neighbor Mixture Models for Non-Gaussian Spatial Processes},
author={Xiaotian Zheng and Athanasios Kottas and Bruno Sansó},
year={2022},
eprint={2107.07736},
archivePrefix={arXiv},
primaryClass={id='stat.ME' full_name='Methodology' is_active=True alt_name=None in_archive='stat' is_general=False description='Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods'}
}

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