Large deviations of the correlogram estimator of the random noise covariance function in the nonlinear regression model

Alexander Ivanov, Yuriy Kozachenko and Kateryna Moskvychova

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 18, 4236-4254

Abstract: In the paper an exponential bound for the probabilities of large deviations of the normalized residual correlogram as an estimator of a random stationary Gaussian noise covariance function in continuous time nonlinear functional regression model is obtained. The strongest known result on weak consistency of the residual correlogram is a corollary of this fact.

Date: 2021
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DOI: 10.1080/03610926.2020.1713369

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