DocumentCode
2054424
Title
Exact minimax strategies for predictive density estimation, data compression and model selection
Author
Liang, Feng ; Barron, Andrew R.
Author_Institution
Inst. of Stat. & Decision Sci., Duke Univ., Durham, NC, USA
fYear
2002
fDate
2002
Firstpage
149
Abstract
The Bayes procedure with uniform prior on location (and log-scale) parameters is shown to be exact minimax optimal for location and scale families in problems of universal data compression, predictive density estimation and model selection.
Keywords
Bayes methods; data compression; information theory; minimax techniques; modelling; parameter estimation; prediction theory; Bayes procedure; exact minimax strategies; linear regression model; location parameters; model selection; predictive density estimation; universal data compression; Data compression; Linear regression; Minimax techniques; Predictive models; Q measurement; Redundancy; Statistical analysis; Statistical distributions; Statistics; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory, 2002. Proceedings. 2002 IEEE International Symposium on
Print_ISBN
0-7803-7501-7
Type
conf
DOI
10.1109/ISIT.2002.1023421
Filename
1023421
Link To Document