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

Yogendra P. Chaubey and Esmaeil Shirazi

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 5, 885-899

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.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2014.990285 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:44:y:2015:i:5:p:885-899

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2014.990285

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().