The principle of large deviations for martingale additive functionals of recurrent Markov processes.
Heck, Matthias K., Maaouia, Faïza (2001)
Electronic Journal of Probability [electronic only]
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Heck, Matthias K., Maaouia, Faïza (2001)
Electronic Journal of Probability [electronic only]
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Del Moral, P., Kouritzin, M.A., Miclo, L. (2001)
Electronic Journal of Probability [electronic only]
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Bressaud, Xavier, Fernández, Roberto, Galves, Antonio (1999)
Electronic Journal of Probability [electronic only]
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Mayer-Wolf, E., Zeitouni, O., Zerner, M.P.W. (2002)
Electronic Journal of Probability [electronic only]
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Arnaud Gloter, Jean Jacod (2001)
ESAIM: Probability and Statistics
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We consider a diffusion process which is observed at times for , each observation being subject to a measurement error. All errors are independent and centered gaussian with known variance . There is an unknown parameter within the diffusion coefficient, to be estimated. In this first paper the case when is indeed a gaussian martingale is examined: we can prove that the LAN property holds under quite weak smoothness assumptions, with an explicit limiting Fisher information. What...
Conlon, Joseph G., Naddaf, Ali (2000)
Electronic Journal of Probability [electronic only]
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Jacka, Saul, Warren, John (2002)
Electronic Journal of Probability [electronic only]
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Steinsaltz, David (1999)
Electronic Journal of Probability [electronic only]
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Schweinsberg, Jason (2000)
Electronic Journal of Probability [electronic only]
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Arnaud Gloter, Jean Jacod (2001)
ESAIM: Probability and Statistics
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We consider a diffusion process which is observed at times for , each observation being subject to a measurement error. All errors are independent and centered gaussian with known variance . There is an unknown parameter to estimate within the diffusion coefficient. In this second paper we construct estimators which are asymptotically optimal when the process is a gaussian martingale, and we conjecture that they are also optimal in the general case.
Evans, Steven N., Peres, Yuval (1998)
Electronic Communications in Probability [electronic only]
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Françoise Pène (2002)
ESAIM: Probability and Statistics
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In this paper, we are interested in the asymptotical behavior of the error between the solution of a differential equation perturbed by a flow (or by a transformation) and the solution of the associated averaged differential equation. The main part of this redaction is devoted to the ascertainment of results of convergence in distribution analogous to those obtained in [10] and [11]. As in [11], we shall use a representation by a suspension flow over a dynamical system. Here, we make...