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Wavelet Estimator in Nonparametric Regression Model with Dependent Error’s Structure

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  • Xing-Cai Zhou
  • Jin-Guan Lin

Abstract

In this article, we consider a nonparametric regression model with replicated observations based on the dependent error’s structure, for exhibiting dependence among the units. The wavelet procedures are developed to estimate the regression function. The moment consistency, the strong consistency, strong convergence rate and asymptotic normality of wavelet estimator are established under suitable conditions. A simulation study is undertaken to assess the finite sample performance of the proposed method.

Suggested Citation

  • Xing-Cai Zhou & Jin-Guan Lin, 2014. "Wavelet Estimator in Nonparametric Regression Model with Dependent Error’s Structure," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(22), pages 4707-4722, November.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:22:p:4707-4722
    DOI: 10.1080/03610926.2012.725500
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    Cited by:

    1. Xuejun Wang & Yi Wu & Rui Wang & Shuhe Hu, 2021. "On consistency of wavelet estimator in nonparametric regression models," Statistical Papers, Springer, vol. 62(2), pages 935-962, April.

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