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Showing 2 results for Isotropic
Ali Mohammadian Mosammam, Serve Mohammadi,
Volume 12, Issue 2 (3-2019)
Abstract
In this paper parameters of spatial covariance functions have been estimated using block composite likelihood method. In this method, the block composite likelihood is constructed from the joint densities of paired spatial blocks. For this purpose, after differencing data, large data sets are splited into many smaller data sets. Then each separated blocks evaluated separately and finally combined through a simple summation. The advantage of this method is that there is no need to inverse and to find determination of high dimensional matrices. The simulation shows that the block composite likelihood estimates as well as the pair composite likelihood. Finally a real data is analysed.
Ronak Jamshidi, Sedigheh Shams,
Volume 13, Issue 2 (2-2020)
Abstract
In this paper, a family of copula functions called chi-square copula family is used for modeling the dependency structure of stationary and isotropic spatial random fields. The dependence structure of this copula is such that, it generalizes the Gaussian copula and flexible for modeling for high-dimensional random vectors and unlike Gaussian copula it allows for modeling of tail asymmetric dependence structures. Since the density function of chi-square copula in high dimension has computational complexity, therefore to estimate its parameters, a composite pairwise likelihood method is used in which only bivariate density functions are used. The purpose of this paper is to investigate the properties of the chi-square copula family, estimating its parameters with the composite pairwise likelihood and its application in spatial interpolation.
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