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Regression estimation under strong mixing data

  • Huijun Guo
  • Youming LiuEmail author
Article
  • 96 Downloads

Abstract

This paper studies multivariate wavelet regression estimators with errors-in-variables under strong mixing data. We firstly prove the strong consistency for non-oscillating and Fourier-oscillating noises. Then, a convergence rate is provided for non-oscillating noises, when an estimated function has some smoothness. Finally, the consistency and convergence rate are discussed for a practical wavelet estimator.

Keywords

Regression estimation Errors-in-variables Strong mixing Practical estimator Wavelets 

Notes

Acknowledgements

This paper is supported by the Beijing Natural Science Foundation (No. 1172001) and the National Natural Science Foundation of China (No. 11771030). The authors would like to thank the referees for their important comments and suggestions.

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Copyright information

© The Institute of Statistical Mathematics, Tokyo 2018

Authors and Affiliations

  1. 1.Department of Applied MathematicsBeijing University of TechnologyBeijingPeople’s Republic of China

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