DocumentCode
3071178
Title
Minimax lower bounds via f-divergences
Author
Guntuboyina, Adityanand
Author_Institution
Dept. of Stat., Yale Univ., New Haven, CT, USA
fYear
2010
fDate
13-18 June 2010
Firstpage
1340
Lastpage
1344
Abstract
We prove a new lower bound for the minimax risk in estimation problems involving f-divergences between the underlying probability measures. The proof just uses the convexity of the function f and is extremely simple. Special cases and straightforward corollaries of our bound include well known inequalities for establishing minimax lower bounds such as Fano´s inequality, Pinsker´s inequality and inequalities based on global entropy conditions.
Keywords
entropy; estimation theory; minimax techniques; probability; Fano inequality; Pinsker inequality; estimation problem; f-divergences; function convexity; global entropy condition; minimax lower bound; minimax risk; probability measures; Density measurement; Entropy; Extraterrestrial measurements; Minimax techniques; Probability; Q measurement; Statistics; Testing; Upper bound;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory Proceedings (ISIT), 2010 IEEE International Symposium on
Conference_Location
Austin, TX
Print_ISBN
978-1-4244-7890-3
Electronic_ISBN
978-1-4244-7891-0
Type
conf
DOI
10.1109/ISIT.2010.5513790
Filename
5513790
Link To Document