On estimation in the multidimensional Gaussian model
Bogusława Bednarek-Kozek (1973)
Applicationes Mathematicae
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Bogusława Bednarek-Kozek (1973)
Applicationes Mathematicae
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Andrzej Sierociński (1985)
Banach Center Publications
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Nicolas Privault, Anthony Réveillac (2011)
ESAIM: Probability and Statistics
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Using integration by parts on Gaussian space we construct a Stein Unbiased Risk Estimator (SURE) for the drift of Gaussian processes, based on their local and occupation times. By almost-sure minimization of the SURE risk of shrinkage estimators we derive an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise.
Nicolas Privault, Anthony Réveillac (2012)
ESAIM: Probability and Statistics
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Using integration by parts on Gaussian space we construct a Stein Unbiased Risk Estimator (SURE) for the drift of Gaussian processes, based on their local and occupation times. By almost-sure minimization of the SURE risk of shrinkage estimators we derive an estimation and de-noising procedure for an input signal perturbed by a continuous-time Gaussian noise.
J. Bartoszewicz (1977)
Applicationes Mathematicae
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Xu Sun, Jinqiao Duan, Xiaofan Li, Xiangjun Wang (2015)
Banach Center Publications
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The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian Lévy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian Lévy noise may have infinite variance. A modified Kalman filter for linear systems with non-Gaussian Lévy noise is devised. It works effectively with reasonable computational cost. Simulation results are presented to illustrate this non-Gaussian filtering...
A. Le-Breton, M. Musiela (1985)
Banach Center Publications
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Beniamin Goldys (1985)
Banach Center Publications
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Marta Ferreira (2013)
Discussiones Mathematicae Probability and Statistics
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In this paper we consider an autoregressive Pareto process which can be used as an alternative to heavy tailed MARMA. We focus on the tail behavior and prove that the tail empirical quantile function can be approximated by a Gaussian process. This result allows to derive a class of consistent and asymptotically normal estimators for the shape parameter. We will see through simulation that the usual estimation procedure based on an i.i.d. setting may fall short of the desired precision. ...
Tomás Cipra, Asunción Rubio (1991)
Trabajos de Estadística
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The dynamic linear model with a non-linear non-Gaussian observation relation is considered in this paper. Masreliez's theorem (see Masreliez's (1975)) of approximate non-Gaussian filtering with linear state and observation relations is extended to the case of a non-linear observation relation that can be approximated by a second-order Taylor expansion.
Jelena Bulatović, Alobodanka Janjić (1979)
Publications de l'Institut Mathématique
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G.B. Nath (1977)
Metrika
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S. Trybuła (1974)
Applicationes Mathematicae
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Ristić, Miroslav M., Popović, Biljana Č. (2001)
Matematichki Vesnik
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Renze, John, Wagon, Stan, Wick, Brian (2001)
Experimental Mathematics
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Ingster, Yu., Suslina, I. (2005)
Zapiski Nauchnykh Seminarov POMI
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Rovskiĭ, V.A. (2004)
Zapiski Nauchnykh Seminarov POMI
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