On nonparametric classification for weakly dependent functional processes

Younso, Ahmad

doi:10.1051/ps/2017002

Younso, Ahmad ¹

¹ Department of mathematical statistics, Faculty of sciences, Damascus University, Syria.

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

Résumé

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 $(ℱ, ρ)$ 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.

MR Zbl

DOI : 10.1051/ps/2017002

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

Affiliations des auteurs :

Younso, Ahmad ¹

¹ Department of mathematical statistics, Faculty of sciences, Damascus University, Syria.

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     author = {Younso, Ahmad},
     title = {On nonparametric classification for weakly dependent functional processes},
     journal = {ESAIM: Probability and Statistics},
     pages = {452--466},
     year = {2017},
     publisher = {EDP Sciences},
     volume = {21},
     doi = {10.1051/ps/2017002},
     mrnumber = {3743922},
     zbl = {1395.62095},
     language = {en},
     url = {https://www.numdam.org/articles/10.1051/ps/2017002/}
}

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%T On nonparametric classification for weakly dependent functional processes
%J ESAIM: Probability and Statistics
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Younso, Ahmad. On nonparametric classification for weakly dependent functional processes. ESAIM: Probability and Statistics, Tome 21 (2017), pp. 452-466. doi: 10.1051/ps/2017002

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