Large deviations of regression parameter estimator in continuous-time models with sub-Gaussian noise

Abstract

A continuous-time regression model with a jointly strictly sub-Gaussian random noise is considered in the paper. Upper exponential bounds for probabilities of large deviations of the least squares estimator for the regression parameter are obtained.

arXiv commentPublished at https://doi.org/10.15559/18-VMSTA102 in the Modern Stochastics: Theory and Applications (https://www.i-journals.org/vtxpp/VMSTA) by VTeX (http://www.vtex.lt/)

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