Classification methods for Hilbert data based on surrogate density

Enea G. Bongiorno and Aldo Goia

Computational Statistics & Data Analysis, 2016, vol. 99, issue C, 204-222

Abstract: An unsupervised and a supervised classification approach for Hilbert random curves are studied. Both rest on the use of a surrogate of the probability density which is defined, in a distribution-free mixture context, from an asymptotic factorization of the small-ball probability. That surrogate density is estimated by a kernel approach from the principal components of the data. The focus is on the illustration of the classification algorithms and the computational implications, with particular attention to the tuning of the parameters involved. Some asymptotic results are sketched. Applications on simulated and real datasets show how the proposed methods work.

Keywords: Density based clustering; Discriminant Bayes rule; Hilbert data; Small-ball probability mixture; Functional principal component; Kernel density estimate (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:99:y:2016:i:c:p:204-222

DOI: 10.1016/j.csda.2016.01.019

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