ABSTRACT
This paper considers wavelet estimations for a regression function based on negatively associated data. We provide upper bounds of mean integrated squared error of wavelet estimators in Besov space. It turns out that our theorem reduces to the theorem of Chesneau and Shirazi [Nonparametric wavelet regression based on biased data. Comm Statist Theory Methods. 2014;43:2642–2658], when the random sample is independent.
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Acknowledgments
The authors would like to thank the referees for their important suggestions and comments.
Disclosure statement
No potential conflict of interest was reported by the authors.