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.

Keywords

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