Abstract
Spatially distributed observations occur naturally in a number of empirical situations; their analysis represents a significant source of theoretical challenge due to the multidirectional dependence among nearest observations. The presence of a dependence often causes the standard statistical methods, instead based on independence assumptions, to fail badly. This paper concerns the problem of discrimination and classification of spatial binary data. It presents a suitable discrimination function based on Markovian automodels and suggests a solution to the allocation problem through a Gibbs sampler-based procedure.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Alfò, M., Postiglione, P. (1999). Discriminant Analysis Using Markovian Automodels. In: Vichi, M., Opitz, O. (eds) Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60126-2_10
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DOI: https://doi.org/10.1007/978-3-642-60126-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65633-3
Online ISBN: 978-3-642-60126-2
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