Discriminant analysis using markovian automodels

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.