Abstract
Suppose y, a d-dimensional (d ≥ 1) vector, is drawn from a mixture of k (k ≥ 2) populations, given by ∏1, ∏2,…,∏ k . We wish to identify the population that is the most likely source of the point y. To solve this classification problem many classification rules have been proposed in the literature. In this study, a new nonparametric classifier based on the transvariation probabilities of data depth is proposed. We compare the performance of the newly proposed nonparametric classifier with classical and maximum depth classifiers using some benchmark and simulated data sets.
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The authors thank the editor and referees for comments that led to an improvement of this paper. This work is partially supported by the National Science Foundation under Grant No. DMS-0604726.
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Billor, N., Abebe, A., Turkmen, A. et al. Classification Based on Depth Transvariations. J Classif 25, 249–260 (2008). https://doi.org/10.1007/s00357-008-9015-7
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DOI: https://doi.org/10.1007/s00357-008-9015-7