Indexed by：
Abstract：
This paper considers a lower bound estimation over L-P (R-d) (1 ＜= p ＜ infinity) risk for d dimensional regression functions in Besov spaces based on biased data. We provide the best possible lower bound up to a Inn factor by using wavelet methods. When the weight function w(x, y) equivalent to 1 and d = 1, our result reduces to Chesneau＇s theorem, see Chesneau (2007). (C) 2015 Elsevier B.V. All rights reserved.
Keyword：
Reprint Author's Address：
Email：
Source ：
STATISTICS & PROBABILITY LETTERS
ISSN： 0167-7152
Year： 2016
Volume： 108
Page： 23-32
0 . 8 0 0
JCR@2022
ESI Discipline： MATHEMATICS;
ESI HC Threshold：71
CAS Journal Grade：4
Cited Count：
WoS CC Cited Count： 11
SCOPUS Cited Count： 11
ESI Highly Cited Papers on the List： 0 Unfold All
WanFang Cited Count：
Chinese Cited Count：
30 Days PV： 7
Affiliated Colleges：
