Copula-Based Semiparametric Models for Spatiotemporal Data
Copula-Based Semiparametric Models for Spatiotemporal Data
复制标题
基于 Copula 的时空数据半参数模型
DOI：
10.1111/biom.13066
复制
发表时间：
2019
期刊：
影响因子：
1.9
通讯作者：
Hering, Amanda S.
中科院分区：
文献类型：
--
作者：
Tang, Yanlin;Wang, Huixia J.;Sun, Ying;Hering, Amanda S.
由于数据的高维性，空间和时间过程的联合分析带来了计算挑战。此外，此类数据通常是非高斯的。在本文中，我们介绍了一种用于分析时空数据的基于 copula 的时空模型，并提出了一种半参数估计器。该算法计算简单，因为它分别对边缘分布和时空依赖性进行建模。所提出的方法不是假设参数分布，而是对边缘分布进行非参数建模，从而提供了更大的灵活性。该方法还提供了一种基于估计的条件分位数在新时间和位置构建点和区间预测的便捷方法。通过模拟研究和对沿俄勒冈州和华盛顿州边界观测到的风速的分析，我们表明，与基于正态性假设的方法相比，我们的方法可以对偏态数据产生更准确的点和区间预测。
The joint analysis of spatial and temporal processes poses computational challenges due to the data's high dimensionality. Furthermore, such data are commonly non-Gaussian. In this paper, we introduce a copula-based spatiotemporal model for analyzing spatiotemporal data and propose a semiparametric estimator. The proposed algorithm is computationally simple, since it models the marginal distribution and the spatiotemporal dependence separately. Instead of assuming a parametric distribution, the proposed method models the marginal distributions nonparametrically and thus offers more flexibility. The method also provides a convenient way to construct both point and interval predictions at new times and locations, based on the estimated conditional quantiles. Through a simulation study and an analysis of wind speeds observed along the border between Oregon and Washington, we show that our method produces more accurate point and interval predictions for skewed data than those based on normality assumptions.