Asymptotic properties of nonparametric M-estimation for mixing functional data

Chen, Jia and Zhang, Lixin (2009) Asymptotic properties of nonparametric M-estimation for mixing functional data. Journal of Statistical Planning and Inference, 139 2: 533-546.


Author Chen, Jia
Zhang, Lixin
Title Asymptotic properties of nonparametric M-estimation for mixing functional data
Journal name Journal of Statistical Planning and Inference   Check publisher's open access policy
ISSN 0378-3758
1873-1171
Publication date 2009-02-01
Year available 2008
Sub-type Article (original research)
DOI 10.1016/j.jspi.2008.05.007
Volume 139
Issue 2
Start page 533
End page 546
Total pages 14
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
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.
Keyword α-Mixing
Asymptotic normality
Consistency
Functional data
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Available online 22 May 2008

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
 
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Created: Thu, 01 Sep 2011, 14:01:19 EST by Dr Jia Chen on behalf of Mathematics