On nonparametric classification for weakly dependent functional processes

Younso, Ahmad

ESAIM: Probability and Statistics, Tome 21 (2017), pp. 452-466.

The purpose of this paper is to investigate the moving window rule of classification to classify functions under mixing conditions. We consider a random variable $X$ taking values in a metric space $(ℱ,\rho )$ with label $Y\in \{0,1\}$. We extend some results on consistency and strong consistency of the moving window rule from the i.i.d. case to the weakly dependent case under mild assumptions. The practical use of the moving window rule will be illustrated through a simulation study. The performance of the moving window rule is investigated.

Classification : 62G08
Mots clés : Bayes rule, training data, moving window rule, mixing condition, consistency

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