Abstract
In this paper a nonparametric method for discriminant analysis is proposed, based on a group separation oriented version of projection pursuit density estimation. Each population is separated in turn from the remaining ones, considered as a whole, by approximating the boundary between them through the composition of some informative directions, chosen according to an appropriate discrimination criterion. A coherent allocation rule is proposed, too. Simulation studies have shown that this method represents a valid solution for problems when the parametric approaches are not flexible enough and sample sizes are too small to use classical nonparametric methods.
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© 1999 Springer-Verlag Berlin · Heidelberg
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Montanari, A., Calò, D.G. (1999). An Empirical Discrimination Algorithm based on Projection Pursuit Density Estimation. 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_13
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DOI: https://doi.org/10.1007/978-3-642-60126-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65633-3
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