DocumentCode
1340254
Title
Computational Limits to Nonparametric Estimation for Ergodic Processes
Author
Takahashi, Hayato
Author_Institution
Inst. of Stat. Math., Tokyo, Japan
Volume
57
Issue
10
fYear
2011
Firstpage
6995
Lastpage
6999
Abstract
A new negative result for nonparametric distribution estimation of binary ergodic processes is shown. The problem of estimation of distribution with any degree of accuracy is studied. Then it is shown that for any countable class of estimators there is a zero-entropy binary ergodic process that is inconsistent with the class of estimators. Our result is different from other negative results for universal forecasting scheme of ergodic processes.
Keywords
entropy; estimation theory; forecasting theory; computational limit; estimators countable class; nonparametric distribution estimation; universal forecasting scheme; zero-entropy binary ergodic process; Accuracy; Convergence; Entropy; Estimation; Stacking; System-on-a-chip; Trajectory; Computable function; cutting and stacking; ergodic process; nonparametric estimation;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/TIT.2011.2165791
Filename
6034744
Link To Document