Non-parametric estimation under strong dependence

Zhibiao Zhao, Yiyun Zhang, Runze Li

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed method has good finite sample performance.

Original languageEnglish (US)
Pages (from-to)4-15
Number of pages12
JournalJournal of Time Series Analysis
Volume35
Issue number1
DOIs
StatePublished - Jan 1 2014

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Nonparametric Estimation
Convergence Rate
Regression Estimation
Function Estimation
Dependent
Nonparametric Estimator
Nonparametric Regression
Regression Function
Range of data
Simulation Study
Model
Nonparametric estimation
Convergence rate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

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Non-parametric estimation under strong dependence. / Zhao, Zhibiao; Zhang, Yiyun; Li, Runze.

In: Journal of Time Series Analysis, Vol. 35, No. 1, 01.01.2014, p. 4-15.

Research output: Contribution to journalArticle

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