MISE of wavelet estimators for regression derivatives with biased strong mixing data. Issue 14 (18th July 2021)

• Record Type: Journal Article
• Authors: Kou, Junke; Chen, Jia; Guo, Huijun
• Is Part Of: Communications in statistics. Volume 50:Issue 14(2021)
• Page Start: 3436
• Page End: 3452
• Publication Date: 2021-07-18
• Subjects: Derivative function estimation -- strong mixing -- mean integrated squared error -- wavelets; 62G07 -- 62G20 -- 42C40; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2019.1704007
• Languages: English
• ISSNs: 0361-0926

Abstract

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.