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Title: Maximum likelihood principle and $I$-divergence: discrete time observations (English)
Author: Michálek, Jiří
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 34
Issue: 3
Year: 1998
Pages: [265]-288
Summary lang: English
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Category: math
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Summary: The paper investigates the relation between maximum likelihood and minimum $I$-divergence estimates of unknown parameters and studies the asymptotic behaviour of the likelihood ratio maximum. Observations are assumed to be done in the discrete time. (English)
Keyword: maximum likelihood estimate
Keyword: information divergence
Keyword: exponential families
Keyword: discrete time process
Keyword: autoregressive sequences
MSC: 62B10
MSC: 62F12
MSC: 62M10
idZBL: Zbl 1274.62066
idMR: MR1640966
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Date available: 2009-09-24T19:16:09Z
Last updated: 2015-03-28
Stable URL: http://hdl.handle.net/10338.dmlcz/135207
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Related article: http://dml.cz/handle/10338.dmlcz/135208
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Reference: [4] Kullback S.: Information Theory and Statistics (in Russian).Nauka, Moscow 1967. Translated from the English original
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Reference: [7] Michálek J.: Yule–Walker estimates and asymptotic $I$-divergence rate.Problems Control Inform. Theory 19 (1990), 5–6, 387–398 Zbl 0744.62126, MR 1086831
Reference: [8] Michálek J.: A method of detecting changes in the behaviour of locally stationary sequences.Kybernetika 31 (1995), 1, 17–29 Zbl 0868.62070, MR 1324658
Reference: [9] Morales D., Pardo L., Vajda I.: About classical and some new statistics for testing hypothesis in parametric models.J. Multivariate Anal. (to appear) MR 1467878
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