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Identifier to cite or link to this item: https://hdl.handle.net/10086/22992

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Title
Nonparametric regression for dependent data in the errors-in-variables problem
Creator
Creator Name
HONDA, Toshio HONDA, Toshio
本田, 敏雄 本田, 敏雄
ホンダ, トシオ ホンダ, トシオ
Affiliation
Affiliation Name
Hitotsubashi University
Rights
(c) 2010ElsevierB.V.Allrightsreserved.
Key Word
Local polynomial regression
Errors-in-variables
Deconvolution
Ordinary smooth case
Supersmooth case
Linear processes
Long-range dependence
Abstract
We consider the nonparametric estimation of the regression functions for dependentdata. Suppose that the covariates are observed with additive errors in the data and we employ nonparametric deconvolution kernel techniques to estimate the regression functions in this paper. We investigate how the strength of time dependence affects the asymptotic properties of the local constant and linear estimators. We treat both short-range dependent and long-range dependent linear processes in a unified way and demonstrate that the long-range dependence (LRD) of the covariates affects the asymptotic properties of the nonparametric estimators as well as the LRD of regressionerrors does.
Publisher
Elsevier B.V.
Issued Date
2010-11
Language
English(eng)
Resource Type
journal article
Version Type
AM
ISSN
03783758
NCID
AA11534614
Source Title
Journal of Statistical Planning and Inference
Volume Number
140
Issue Number
11
Page Start
3409
Page End
3424
Appears in Collections
010a Journal Articles　=　雑誌掲載論文
URL
https://hdl.handle.net/10086/22992