COPULA-BASED SEMIVARIOGRAM MODEL AND COMPUTATIONAL REDUCTION ON THE SEQUENTIAL KRIGING ALGORITHM - Febrian Umbara (NIM 20105015), Rian
ABSTRACT: The dependency of spatial data can be quantified through semivariogram, which is an important part in estimation processes. In this thesis, a method for constructing semivariogram model using copula formalism is discussed. The model is constructed based on median regression which is derived from a copula. By using copula formalism, it is no longer necessary to force using any a priori semivariogram models. After the model is obtained, it is used in estimation processes using ordinary kriging and sequential kriging methods. The computational complexity of the sequential kriging method is much lower than that of the ordinary kriging method and the results of those methods are not significantly different. The computational complexity of the sequential kriging can still be reduced so that its algorithm will be more efficient.