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Interpolating a skewed conditional spatial random field with missing data iscumbersome in the absence of Gaussianity assumptions. Maintaining spatialhomogeneity and continuity around the observed random spatial point is alsochallenging, especially when interpolating along a spatial surface, focusing onthe boundary points as a neighborhood. Otherwise, the point far away from onemay appear the closest to another. As a result, importing the hierarchicalclustering concept on the spatial random field is as convenient as developingthe copula with the interface of the Expectation-Maximization algorithm andconcurrently utilizing the idea of the Bayesian framework. This paperintroduces a spatial cluster-based C-vine copula and a modified Gaussian kernelto derive a novel spatial probability distribution. Another investigation inthis paper uses an algorithm in conjunction with a different parameterestimation technique to make spatial-based copula interpolation more compatibleand efficient. We apply the proposed spatial interpolation approach to the airpollution of Delhi as a crucial circumstantial study to demonstrate this newlydeveloped novel spatial estimation technique.

Spatial Cluster-based Copula Model to Interpolate Skewed Conditional Spatial Random Field