| Issue |
ESAIM: PS
Volume 21, 2017
|
|
|---|---|---|
| Page(s) | 452 - 466 | |
| DOI | https://doi.org/10.1051/ps/2017002 | |
| Published online | 08 January 2018 | |
On nonparametric classification for weakly dependent functional processes
Department of mathematical statistics, Faculty of sciences, Damascus University, Syria.
ahyounso@yahoo.fr
Received: 22 June 2016
Revised: 6 January 2017
Accepted: 15 February 2017
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 ∈ {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.
Mathematics Subject Classification: 62G08
Key words: Bayes rule / training data / moving window rule / mixing condition / consistency
© EDP Sciences, SMAI, 2017
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