DocumentCode
3508318
Title
Some limits to nonparametric estimation for ergodic processes
Author
Takahashi, Hayato
Author_Institution
Inst. of Stat. Math., Tokyo, Japan
fYear
2011
fDate
July 31 2011-Aug. 5 2011
Firstpage
2497
Lastpage
2498
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. We also introduce a related result by B. Weiss.
Keywords
entropy; nonparametric statistics; statistical distributions; binary ergodic processes; entropy; nonparametric distribution estimation; universal forecasting scheme; Accuracy; Convergence; Entropy; Estimation; Information theory; Nickel; System-on-a-chip; computable function; cutting and stacking; ergodic process; nonparametric estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on
Conference_Location
St. Petersburg
ISSN
2157-8095
Print_ISBN
978-1-4577-0596-0
Electronic_ISBN
2157-8095
Type
conf
DOI
10.1109/ISIT.2011.6034015
Filename
6034015
Link To Document