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|Title:||Asymptotic properties of nonparametric M-estimation for mixing functional data|
|Citation:||Journal of Statistical Planning and Inference, 2008; 139(2):533-546|
|Publisher:||Elsevier Science BV|
|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|
|Appears in Collections:||Economics publications|
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