Nonparametric M-regression for functional ergodic data

Let (Xi,Yi)i=1,…,n be a sequence of stationary ergodic processes valued in F×R, where F is a semi-metric space. We consider the problem of estimating the regression function of Yi given Xi by the robust M-estimation method. The principal aim of this work is to prove the almost complete convergence (with rate) for the proposed estimator. This result is obtained under a stationary ergodic process assumption, without using traditional mixing conditions.

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Year of publication:	2013
Authors:	Gheriballah, Abdelkader ; Laksaci, Ali ; Sekkal, Soumeya
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 83.2013, 3, p. 902-908
Publisher:	Elsevier
Subject:	Functional data \| Kernel estimate \| Nonparametric model \| Robust estimation \| Ergodic data

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011040013