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Teoriya Veroyatnostei i ee Primeneniya, 2001, Volume 46, Issue 2, Pages 233–246
DOI: https://doi.org/10.4213/tvp3916 (Mi tvp3916) 		 
This article is cited in 1 scientific paper (total in 1 paper)

On Some Estimation Problems with Information Constraints

M. V. Burnasheva, T. S. Hanb, Shun-ichi Amaric

a A. A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences
b University of Electro-Communications, Graduate School of Information Systems
c RIKEN Brain Science Institute
Full-text PDF (1349 kB) Citations (1)
DOI: https://doi.org/10.4213/tvp3916
Abstract: This paper is the second part of [M. V. Burnashev, Sh. Amari, and T. S. Han, Theory Probab. Appl., 45 (2000), pp. 558–568]. A parameter estimation problem is considered where some part of the data cannot be directly observed. Our helper observes those data and can send us some limited amount of information about them. What kind of information allows us to get a minimal mean-square error in a parameter estimate? In particular, what is the minimal information required to get the same mean-square error as when we directly observe all the data? Some upper bounds for that minimal amount of information and some related results are obtained.
Keywords: parameter estimate, mean-square error, Fisher information, rate of transmission, critical rate.
Received: 22.10.1998
English version:
Theory of Probability and its Applications, 2002, Volume 46, Issue 2, Pages 214–225
DOI: https://doi.org/10.1137/S0040585X97978889
Bibliographic databases:
Language: Russian
Citation: M. V. Burnashev, T. S. Han, Shun-ichi Amari, “On Some Estimation Problems with Information Constraints”, Teor. Veroyatnost. i Primenen., 46:2 (2001), 233–246; Theory Probab. Appl., 46:2 (2002), 214–225
Citation in format AMSBIB
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\transl
\jour Theory Probab. Appl.
\yr 2002
\vol 46
\issue 2
\pages 214--225
\crossref{https://doi.org/10.1137/S0040585X97978889}
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  • https://www.mathnet.ru/eng/tvp/v46/i2/p233
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