论文标题
基于空间簇的副群模型，以插值偏斜的条件空间随机场
Spatial Cluster-based Copula Model to Interpolate Skewed Conditional Spatial Random Field
论文作者
论文摘要
在没有高斯假设的情况下，与缺少数据的偏斜条件空间随机场插值很麻烦。在观察到的随机空间点周围保持空间均匀性和连续性也很具有挑战性，尤其是当沿空间表面插值时，重点是作为邻域的边界点。否则，远离一个的要点可能出现在最接近另一个的地方。结果，在空间随机字段上导入层次聚类概念与开发具有期望最大化算法的接口并同时利用贝叶斯框架的想法的copula一样方便。本文介绍了基于空间簇的C-vine Copula和修改的高斯核，以得出新型的空间概率分布。本文的另一项研究将算法与不同的参数估计技术结合使用，以使基于空间的Copula插值更加兼容和有效。我们将提出的空间插值方法应用于德里的空气污染，作为一项至关重要的环境研究，以证明这种新开发的新型空间估计技术。
Interpolating a skewed conditional spatial random field with missing data is cumbersome in the absence of Gaussianity assumptions. Maintaining spatial homogeneity and continuity around the observed random spatial point is also challenging, especially when interpolating along a spatial surface, focusing on the boundary points as a neighborhood. Otherwise, the point far away from one may appear the closest to another. As a result, importing the hierarchical clustering concept on the spatial random field is as convenient as developing the copula with the interface of the Expectation-Maximization algorithm and concurrently utilizing the idea of the Bayesian framework. This paper introduces a spatial cluster-based C-vine copula and a modified Gaussian kernel to derive a novel spatial probability distribution. Another investigation in this paper uses an algorithm in conjunction with a different parameter estimation technique to make spatial-based copula interpolation more compatible and efficient. We apply the proposed spatial interpolation approach to the air pollution of Delhi as a crucial circumstantial study to demonstrate this newly developed novel spatial estimation technique.
