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MISE of wavelet estimators for regression derivatives with biased strong mixing data

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  • Junke Kou
  • Jia Chen
  • Huijun Guo

Abstract

Using a wavelet basis, this article considers the mean integrated squared error (MISE) of linear and non linear wavelet estimators for regression derivatives r(d)(x) based on biased strong mixing data. It turns out that the convergence rates coincide with those of Chesneau and Shirazi’s (Communication in Statistics-Theory and Methods, 2014), when d = 0 and the random sample is independent.

Suggested Citation

  • Junke Kou & Jia Chen & Huijun Guo, 2021. "MISE of wavelet estimators for regression derivatives with biased strong mixing data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(14), pages 3436-3452, July.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:14:p:3436-3452
    DOI: 10.1080/03610926.2019.1704007
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