Some Spatial depth Classifiers




classifiers, data depth, spatial depth, kernel, weight


Several multivariate depth functions have been proposed in literature of which some satisfy all the conditions for statistical depth functions while some do not. Spatial depth is known to be invariant only to spherical and shift transformations. In this paper, the possibility of using different versions of spatial depth in classification is considered. We extend the notions of maximal depth classifiers for covariance-adjusted, weighted and kernel-based versions of spatial depth. The performance of the classifiers is considered and compared with some existing classification methods using simulated and real datasets.


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