Convergence rate for the estimation of impulse response function in the space of continuous functions

Автор(и)

DOI:

https://doi.org/10.17721/1812-5409.2018/3.4

Анотація

The problem of estimation of a stochastic linear system has been a matter of active research for the last years. One of the simplest models considers a ‘black box’ with some input and a certain output. The input may be single or multiple and there is the same choice for the output. This generates a great amount of models that can be considered. The sphere of applications of these models is very extensive, ranging from signal processing and automatic control to econometrics (errors-in-variables models). In this paper a time-invariant continuous linear system is considered with a real-valued impulse response function. We assume that impulse function is square-integrable. Input signal is supposed to be Gaussian stationary stochastic process with known spectral density. A sample input–output cross-correlogram is taken as an estimator of the response function. An upper bound for the tail of the distribution of the supremum of the estimation error is found that gives a convergence rate of estimator to impulse response function.

Key words: impulse response function, linear time-invariant system (LTI), Gaussian process, crosscorrelogram.

Pages of the article in the issue: 30 - 36

Language of the article: Ukrainian

Посилання

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BULDYGIN V., UTZET F., ZAIATS V. (2004) Asymptotic normality of crosscoorrelogram estimators of the response function Statistical Infernce for Stochastic Processes, 7, 1–34.

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KOZACHENKO YU., PASHKO A., ROZORA I. (2007) Simulation of Stochastic Processes and fields, Zadruga, Kyi. (in Ukrainian)

KOZACHENKO YU., ROZORA I. (2015) On cross-correlogram estimators of impulse response functions Theor. Probability and Math. Statist., 93, 75-86.

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Завантаження

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Розділ

Алгебра, геометрія та теорія ймовірностей

Як цитувати

Rozora, I. V. (2018). Convergence rate for the estimation of impulse response function in the space of continuous functions. Вісник Київського національного університету імені Тараса Шевченка. Фізико-математичні науки, 3, 30-36. https://doi.org/10.17721/1812-5409.2018/3.4

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