DocumentCode
2337625
Title
Lower bounds on expected redundancy
Author
Yu, Bin
Author_Institution
Dept. of Stat., California Univ., Berkeley, CA, USA
fYear
1994
fDate
27-29 Oct 1994
Firstpage
15
Abstract
This paper focuses on lower bound results on expected redundancy for universal compression of i.i.d. data from parametric and nonparametric families. Two types of lower bounds are reviewed. One is Rissanen´s almost pointwise lower bound and its extension to the nonparametric case. The other is minimax lower bounds, for which a new proof is given in the nonparametric case
Keywords
data compression; minimax techniques; redundancy; stochastic processes; IID data; Rissanen´s lower bound; expected redundancy; lower bounds; minimax lower bounds; nonparametric families; parametric families; universal compression; Estimation error; Hypercubes; Minimax techniques; Mutual information; Parametric statistics; Redundancy; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location
Alexandria, VA
Print_ISBN
0-7803-2761-6
Type
conf
DOI
10.1109/WITS.1994.513857
Filename
513857
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