Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/55787
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Type: Journal article
Title: Asymptotic properties of nonparametric M-estimation for mixing functional data
Author: Chen, J.
Zhang, L.
Citation: Journal of Statistical Planning and Inference, 2008; 139(2):533-546
Publisher: Elsevier Science BV
Issue Date: 2008
ISSN: 0378-3758
Statement of
Responsibility: 
Jia Chen and Lixin Zhang
Abstract: We investigate the asymptotic behavior of a nonparametric M-estimator of a regression function for stationary dependent processes, where the explanatory variables take values in some abstract functional space. Under some regularity conditions, we give the weak and strong consistency of the estimator as well as its asymptotic normality. We also give two examples of functional processes that satisfy the mixing conditions assumed in this paper. Furthermore, a simulated example is presented to examine the finite sample performance of the proposed estimator.
Keywords: α-Mixing; Asymptotic normality; Consistency; Functional data; Nonparametric M-estimation
RMID: 0020092504
DOI: 10.1016/j.jspi.2008.05.007
Appears in Collections:Economics publications

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