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

Junke Kou, Jia Chen and Huijun Guo

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 14, 3436-3452

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.

Date: 2021
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DOI: 10.1080/03610926.2019.1704007

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