A method of trend recognition in time series
Jiří Michálek, Jiří Skřivánek (1992)
Kybernetika
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Displaying similar documents to “A method of detecting changes in the behaviour of locally stationary sequences”
A method of trend recognition in time series
A method of detecting changes in the behaviour of a random sequence based on the Bayes approach
Jiří Michálek, Jiří Skřivánek (1993)
Kybernetika
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Maximum likelihood principle and -divergence: continuous time observations
Jiří Michálek (1998)
Kybernetika
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The paper investigates the relation between maximum likelihood and minimum -divergence estimates of unknown parameters and studies the asymptotic behaviour of the likelihood ratio maximum. Observations are assumed to be done in the continuous time.
Asymptotic behavior of the empirical process for gaussian data presenting seasonal long-memory
Mohamedou Ould Haye (2002)
ESAIM: Probability and Statistics
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We study the asymptotic behavior of the empirical process when the underlying data are gaussian and exhibit seasonal long-memory. We prove that the limiting process can be quite different from the limit obtained in the case of regular long-memory. However, in both cases, the limiting process is degenerated. We apply our results to von–Mises functionals and -Statistics.