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On MISE of a Non linear Wavelet Estimator of the Regression Function Based on Biased Data under Strong Mixing

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  • Yogendra P. Chaubey
  • Esmaeil Shirazi

Abstract

In this paper, we consider the adaptation of the non linear wavelet-based estimator of the regression function for the biased data setup under strong mixing. We provide an asymptotic sharp bound for the mean integrated squared error (MISE) of the estimator, that is nearly optimal in the minimax sense over a large range of Besov function classes.

Suggested Citation

  • Yogendra P. Chaubey & Esmaeil Shirazi, 2015. "On MISE of a Non linear Wavelet Estimator of the Regression Function Based on Biased Data under Strong Mixing," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(5), pages 885-899, March.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:5:p:885-899
    DOI: 10.1080/03610926.2014.990285
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    Cited by:

    1. Huijun Guo & Youming Liu, 2019. "Regression estimation under strong mixing data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 553-576, June.

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