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Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geos...

詳細記述

保存先:
書誌詳細
出版年:J Am Stat Assoc
主要な著者: Datta, Abhirup, Banerjee, Sudipto, Finley, Andrew O., Gelfand, Alan E.
フォーマット: Artigo
言語:Inglês
出版事項: 2016
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC5927603/
https://ncbi.nlm.nih.gov/pubmed/29720777
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/01621459.2015.1044091
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