Maximum likelihood principle and -divergence: discrete time observations
Kybernetika (1998)
- Volume: 34, Issue: 3, page [265]-288
- ISSN: 0023-5954
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topMichálek, Jiří. "Maximum likelihood principle and $I$-divergence: discrete time observations." Kybernetika 34.3 (1998): [265]-288. <http://eudml.org/doc/33354>.
@article{Michálek1998,
abstract = {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.},
author = {Michálek, Jiří},
journal = {Kybernetika},
keywords = {maximum likelihood estimate; information divergence; exponential families; discrete time process; autoregressive sequences; maximum likelihood estimate; information divergence; exponential families; discrete time process; autoregressive sequences},
language = {eng},
number = {3},
pages = {[265]-288},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Maximum likelihood principle and $I$-divergence: discrete time observations},
url = {http://eudml.org/doc/33354},
volume = {34},
year = {1998},
}
TY - JOUR
AU - Michálek, Jiří
TI - Maximum likelihood principle and $I$-divergence: discrete time observations
JO - Kybernetika
PY - 1998
PB - Institute of Information Theory and Automation AS CR
VL - 34
IS - 3
SP - [265]
EP - 288
AB - 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.
LA - eng
KW - maximum likelihood estimate; information divergence; exponential families; discrete time process; autoregressive sequences; maximum likelihood estimate; information divergence; exponential families; discrete time process; autoregressive sequences
UR - http://eudml.org/doc/33354
ER -
References
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