A latent class MDS model with spatial constraints for non-stationary spatial covariance estimation |
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Authors: | J F Vera R Macías J M Angulo |
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Institution: | (1) Department of Statistics and Operations Research, University of Granada, Campus Fuente Nueva s/n, 18071 Granada, Spain |
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Abstract: | Multidimensional scaling (MDS) has played an important role in non-stationary spatial covariance structure estimation and
in analyzing the spatiotemporal processes underlying environmental studies. A combined cluster-MDS model, including geographical
spatial constraints, has been previously proposed by the authors to address the estimation problem in oversampled domains
in a least squares framework. In this paper is formulated a general latent class model with spatial constraints that, in a
maximum likelihood framework, allows to partition the sample stations into classes and simultaneously to represent the cluster
centers in a low-dimensional space, while the stations and clusters retain their spatial relationships. A model selection
strategy is proposed to determine the number of latent classes and the dimensionality of the problem. Real and artificial
data sets are analyzed to test the performance of the model. |
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Keywords: | Multidimensional scaling k-means clustering Analysis of dispersion Spatiotemporal processes Nonstationarity |
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