Academic literature on the topic 'Discrete-time filtrations'

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Journal articles on the topic "Discrete-time filtrations"

1

KOVACEVIC, RAIMUND M., and GEORG CH PFLUG. "ARE TIME CONSISTENT VALUATIONS INFORMATION MONOTONE?" International Journal of Theoretical and Applied Finance 17, no. 01 (2014): 1450003. http://dx.doi.org/10.1142/s0219024914500034.

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Multi-period risk functionals assign a risk value to discrete-time stochastic processes. While convexity and monotonicity extend in straightforward manner from the single-period case, the role of information is more problematic in the multi-period situation. In this paper, we define multi-period functionals in such a way that the development of available information over time (expressed as a filtration) enters explicitly the definition of the functional. This allows to define and study the property of information monotonicity, i.e. monotonicity w.r.t. increasing filtrations. On the other hand,
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2

Kowalczuk, Zdzislaw, and Piotr Suchomski. "Discrete-Time Generalized Predictive Control with Anticipated Filtration." IFAC Proceedings Volumes 29, no. 1 (1996): 5238–43. http://dx.doi.org/10.1016/s1474-6670(17)58513-4.

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3

Borisov, A. V., and G. B. Miller. "Analysis and Filtration of Special Discrete-Time Markov Processes. II. Optimal Filtration." Automation and Remote Control 66, no. 7 (2005): 1125–36. http://dx.doi.org/10.1007/s10513-005-0153-7.

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4

Savrassov, Ju S. "Algorithms of filtration and extrapolation for discrete-time dynamical systems." Acta Applicandae Mathematicae 30, no. 3 (1993): 193–263. http://dx.doi.org/10.1007/bf00995471.

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5

Martyr, Randall, John Moriarty, and Magnus Perninge. "Discrete-time risk-aware optimal switching with non-adapted costs." Advances in Applied Probability 54, no. 2 (2022): 625–55. http://dx.doi.org/10.1017/apr.2021.44.

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AbstractWe solve non-Markovian optimal switching problems in discrete time on an infinite horizon, when the decision-maker is risk-aware and the filtration is general, and establish existence and uniqueness of solutions for the associated reflected backward stochastic difference equations. An example application to hydropower planning is provided.
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Popiński, Waldemar. "Wavelet transform for time-frequency representation and filtration of discrete signals." Applicationes Mathematicae 23, no. 4 (1996): 433–48. http://dx.doi.org/10.4064/am-23-4-433-448.

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7

Obinabo, E. C., and C. E. Ojieabu. "Measurement Noise Filtration and State Estimation of a Discrete-Time Stochastic Process." International Journal of Soft Computing 5, no. 2 (2010): 29–34. http://dx.doi.org/10.3923/ijscomp.2010.29.34.

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8

Borisov, A. V., and G. B. Miller. "Analysis and Filtration of Special Discrete-Time Markov Processes. I. Martingale Representation." Automation and Remote Control 66, no. 6 (2005): 953–62. http://dx.doi.org/10.1007/s10513-005-0138-6.

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9

Duda, Zdzislaw. "State estimation in a decentralized discrete time LQG control for a multisensor system." Archives of Control Sciences 27, no. 1 (2017): 29–39. http://dx.doi.org/10.1515/acsc-2017-0002.

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Abstract In the paper a state filtration in a decentralized discrete time Linear Quadratic Gaussian problem formulated for a multisensor system is considered. Local optimal control laws depend on global state estimates and are calculated by each node. In a classical centralized information pattern the global state estimators use measurements data from all nodes. In a decentralized system the global state estimates are computed at each node using local state estimates based on local measurements and values of previous controls, from other nodes. In the paper, contrary to this, the controls are
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10

CHEN, YANHONG, and YIJUN HU. "SET-VALUED DYNAMIC RISK MEASURES FOR BOUNDED DISCRETE-TIME PROCESSES." International Journal of Theoretical and Applied Finance 23, no. 03 (2020): 2050017. http://dx.doi.org/10.1142/s021902492050017x.

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In this paper, we study how to evaluate the risk of a financial portfolio, whose components may be dependent and come from different markets or involve more than one kind of currencies, while we also take into consideration the uncertainty about the time value of money. Namely, we introduce a new class of risk measures, named set-valued dynamic risk measures for bounded discrete-time processes that are adapted to a given filtration. The time horizon can be finite or infinite. We investigate the representation results for them by making full use of Legendre–Fenchel conjugation theory for set-va
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