Bayesian spatially varying coefficient models in the spBayes R package

Bayesian spatially varying coefficient models in the spBayes R package
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spBayes R 包中的贝叶斯空间变化系数模型

DOI:
10.1016/j.envsoft.2019.104608
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发表时间:
2020
影响因子:
4.9
通讯作者:
Banerjee, Sudipto
Banerjee, Sudipto
中科院分区:
环境科学与生态学2区
文献类型:
--
作者:
Finley, Andrew O.;Banerjee, Sudipto

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本文描述并说明了espbayes (version 0.4-2) R包中用于拟合空间变化系数模型的新功能。newspssvc函数使用计算效率高的马尔可夫链蒙特卡罗算法,并扩展了只适合空间变截距回归模型的currentspbayes函数,以适应回归设计矩阵中任何列集的独立或多元高斯过程随机效应。讨论并说明了spsvcare的新添加的OpenMP并行化选项,以及用于关节和逐点预测和模型拟合诊断的辅助函数。通过中欧的pm10分析说明了所提出模型的实用性。
This paper describes and illustrates new functionality for fitting spatially varying coefficients models in thespBayes(version 0.4–2) R package. The newspSVCfunction uses a computationally efficient Markov chain Monte Carlo algorithm and extends currentspBayesfunctions, that fit only space-varying intercept regression models, to fit independent or multivariate Gaussian process random effects for any set of columns in the regression design matrix. Newly added OpenMP parallelization options forspSVCare discussed and illustrated, as well as helper functions for joint and point-wise prediction and model fit diagnostics. The utility of the proposed models is illustrated using a PM10analysis over central Europe.
DOI: 10.1214/16-aoas931
发表时间: 2016-09
期刊: The annals of applied statistics
影响因子: --
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发表时间: 2017-01-01
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发表时间: 2015
期刊: Journal of the Royal Statistical Society: Series C (Applied Statistics)
影响因子: --
作者:
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