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Markovské semigrupy
Žák, František ; Maslowski, Bohdan (advisor) ; Štěpán, Josef (referee)
In the presented work we study the existence of periodic solution to infinite dimensional stochastic equation with periodic coefficients driven by Cylindrical Wiener process. Used theory of infinite dimensional stochastic equations in Hilbert spaces and Markov processes is summarized in the first two chapters. In the third and last chapter we present the result itself. Necessary technical background mostly from operator theory is encapsulated in the Appendix. The proof of existence of periodic solution of corresponding equation is a combination of arguments by Khasminskii, which ensure under suitable conditions the existence of periodic Markov process, and the results of Da Prato, G¸atatrek and Zabczyk for the existence of invariant measure for homogeneous stochastic equation in Hilbert spaces. At the end we derive sufficient condition for the existence of periodic solution in the language of coefficients using the work of Ichikawa and illustrate the results by the example of Stochastic PDE. The work is written in English.
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Nonnegative time series
Ročková, Veronika ; Anděl, Jiří (advisor) ; Štěpán, Josef (referee)
Models for non-negative time series nd their usefulness in many diverse areas of applications (hydrology, medicine, nance). The non-negative nature of the observations has been utilized for deriving estimators with superior asymptotic properties. For the purposes of estimation, it is necessary to recognize the situations when the estimated model indeed de nes a non-negative time series. Such non-negativity conditions can then be used as a basis for constrained optimization. The main thrust of this work is to review the non-negativity conditions currently available for ARMA models and, more importantly, to generalize the existing results for some models for which the explicit result was missing. We center our discussion mainly on univariate models. However, we note that the pursued ideas are directly applicable also for multivariate time series. This observation enables determination of some readily obtainable conditions for lower order vector valued Autoregressive Moving Average models.
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Robust filtering
Mach, Tibor ; Dostál, Petr (advisor) ; Štěpán, Josef (referee)
This work is focused on the problem of filtering of random processes and on the construction of a stochastic integral with a measureable parameter. This integral is used to devise filtration equations for a random process which is based on a model motivated by a financial application. The method used to devise them and the equations themselves are then compared with the so called optional filtering from the book Markov processes and Martingales by Rogers and Williams, while the definition of the optional projection is extended so it is possible to correct a~mistake in a proposition in the aforementioned book. Powered by TCPDF (www.tcpdf.org)
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The identification function for the convergence in probability with an application in the estimation theory
Kříž, Pavel ; Štěpán, Josef (advisor) ; Hlubinka, Daniel (referee)
In the present work we introduce the concept of probability limit identification function (PLIF) as it is done in [6]. This function identifies almost surely the value of the probability limit of a sequence of random variables on the basis of one realization of the sequence. According to the same article we show the construction of PLIF for real valued random variables from the special PLIF for 0-1 valued random variables. Following the method described in [8] we prove the existence of the universal PLIF for real valued random variables under the continuum hypothesis. Next we show that there are no borel measurable special PLIFs for 0-1 valued random variables (as well as PLIFs for real valued random variables). We use the proof that is published in [2]. Then we extend the construction of PLIF from R to any separable metrizable topological space. This PLIF may be used e.g. for creating functional representations of stochastic integrals and weak solutions of stochastic differential equations.
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Three proofs of a limit theorem
Marcinčín, Martin ; Štěpán, Josef (advisor) ; Beneš, Viktor (referee)
We show three diferent proofs of the central limit theorem using elementary methods. The central limit theorem with the Feller - Lindeberg condition is proven using a convergence of charakteristic functions and Fejer theorem about uniform convergence of trigonometric polynoms on a bounded interval. The second proof is based on the fact that convergence in distribution is equivalent to convergence of means of functions with all derivatives bounded. The central limit theorem for sums of independent random variables with all moments finite is shown using convergence of all moments and determinacy of normal distribution by its moments.
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