TY - GEN
T1 - Localized upper and lower bounds for some estimation problems
AU - Zhang, Tong
PY - 2005
Y1 - 2005
N2 - We derive upper and lower bounds for some statistical estimation problems. The upper bounds are established for the Gibbs algorithm. The lower bounds, applicable for all statistical estimators, match the obtained upper bounds for various problems. Moreover, our frame-work can be regarded as a natural generalization of the standard minimax framework, in that we allow the performance of the estimator to vary for different possible underlying distributions according to a pre-defined prior.
AB - We derive upper and lower bounds for some statistical estimation problems. The upper bounds are established for the Gibbs algorithm. The lower bounds, applicable for all statistical estimators, match the obtained upper bounds for various problems. Moreover, our frame-work can be regarded as a natural generalization of the standard minimax framework, in that we allow the performance of the estimator to vary for different possible underlying distributions according to a pre-defined prior.
UR - https://www.scopus.com/pages/publications/26944502337
UR - https://www.scopus.com/inward/citedby.url?scp=26944502337&partnerID=8YFLogxK
U2 - 10.1007/11503415_35
DO - 10.1007/11503415_35
M3 - Conference contribution
AN - SCOPUS:26944502337
SN - 3540265562
SN - 9783540265566
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 516
EP - 530
BT - Learning Theory - 18th Annual Conference on Learning Theory, COLT 2005, Proceedings
PB - Springer
T2 - 18th Annual Conference on Learning Theory, COLT 2005 - Learning Theory
Y2 - 27 June 2005 through 30 June 2005
ER -