A Simple Conditional Approach for Generating Spatial Correlated Binary Data
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 4, July 2015, Pages: 305-311
Received: Jun. 29, 2015; Accepted: Jul. 8, 2015; Published: Jul. 17, 2015
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Authors
Renhao Jin, School of Information, Beijing Wuzi University, Beijing, China
Tao Liu, School of Information, Beijing Wuzi University, Beijing, China
Fang Yan, School of Information, Beijing Wuzi University, Beijing, China
Jie Zhu, School of Information, Beijing Wuzi University, Beijing, China
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Abstract
Generating a spatial random field in which the observations are binary random variables with a particular covariance function may be impossible, because there are restrictions on the parameters of Bernoulli variables. This paper develops a conditional method based from spatial GLMM for generating spatial correlated binary data, which can generate spatial correlated binary data, with the variograms of the simulated data are similar to the variograms of the corresponding latent Gaussian random field. However, the closed form for their spatial correlation is not available specifically.
Keywords
Spatial Binary Data, Generalized Linear Mixed Model, Variogram
To cite this article
Renhao Jin, Tao Liu, Fang Yan, Jie Zhu, A Simple Conditional Approach for Generating Spatial Correlated Binary Data, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 4, 2015, pp. 305-311. doi: 10.11648/j.ajtas.20150404.21
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