Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R
Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R
复制标题
具有空间变化参数的协方差函数的局部似然估计：R 的 convoSPAT 包
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Catherine A. Calder
中科院分区：
文献类型：
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作者：
M. Risser;Catherine A. Calder
尽管基于卷积的非平稳空间建模方法引起了人们的兴趣和吸引力，但目前尚不存在用于模型拟合的现成软件。基于卷积的模型非常灵活，但众所周知，即使数据集相对较小，也很难拟合。由于普遍缺乏模型拟合的预打包选项，因此很难将非平稳建模中的新方法与其他现有方法进行比较，因此大多数新模型都只是与平稳模型进行比较。使用基于卷积的方法，我们为空间高斯过程模型提出了一种新的非平稳协方差函数，它允许以两种方式进行有效计算：首先，通过离散混合或“混合分量”模型表示空间变化的参数，其次，通过局部似然方法估计混合分量参数。为了方便地进行基于卷积的非平稳空间模型的计算，本文还介绍并描述了 R 的 convoSPAT 包。该非平稳模型适用于合成数据集和涉及年降水量的实际数据应用，以展示该包的功能。
In spite of the interest in and appeal of convolution-based approaches for nonstationary spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convolution-based models are highly flexible yet notoriously difficult to fit, even with relatively small data sets. The general lack of pre-packaged options for model fitting makes it difficult to compare new methodology in nonstationary modeling with other existing methods, and as a result most new models are simply compared to stationary models. Using a convolution-based approach, we present a new nonstationary covariance function for spatial Gaussian process models that allows for efficient computing in two ways: first, by representing the spatially-varying parameters via a discrete mixture or "mixture component" model, and second, by estimating the mixture component parameters through a local likelihood approach. In order to make computation for a convolution-based nonstationary spatial model readily available, this paper also presents and describes the convoSPAT package for R. The nonstationary model is fit to both a synthetic data set and a real data application involving annual precipitation to demonstrate the capabilities of the package.