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Large deviations of regression parameter estimator in continuous-time models with sub-Gaussian noise
Volume 5, Issue 2 (2018), pp. 191–206
Alexander V. Ivanov   Igor V. Orlovskyi  

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https://doi.org/10.15559/18-VMSTA102
Pub. online: 7 May 2018      Type: Research Article      Open accessOpen Access

Received
31 January 2018
Revised
19 April 2018
Accepted
22 April 2018
Published
7 May 2018

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.

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Keywords
Continuous-time nonlinear regression jointly strictly sub-Gaussian noise least squares estimator probabilities of large deviations

MSC2010
60G50 (primary) 65B10 (primary) 60G15 (primary) 40A05 (secondary)

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