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

DOI10.15559/18-VMSTA102zbMath1391.60100arXiv1806.03842OpenAlexW3102682440MaRDI QIDQ1645196

Igor V. Orlovskyi, Alexander V. Ivanov

Publication date: 28 June 2018

Published in: Modern Stochastics. Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.03842

zbMATH Keywords

least squares estimatorprobabilities of large deviationscontinuous-time nonlinear regressionjointly strictly sub-Gaussian noise

Mathematics Subject Classification ID

Gaussian processes (60G15) Sums of independent random variables; random walks (60G50) Convergence and divergence of series and sequences (40A05) Numerical summation of series (65B10)

Related Items (3)

Large deviations of the correlogram estimator of the random noise covariance function in the nonlinear regression model ⋮ Large deviation inequalities of Bayesian estimator in nonlinear regression models ⋮ On the Whittle estimator for linear random noise spectral density parameter in continuous-time nonlinear regression models

Cites Work

• The large deviation for the least squares estimator of nonlinear regression model based on WOD errors
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• On the convergence of sums of independent Banach space valued random variables
• On the measurability and consistency of minimum contrast estimates
• An Asymptotic Expansion for the Distribution of the Least Squares Estimator of the Non-Linear Regression Parameter
• Large deviations of regression parameter estimate in the models with stationary sub-Gaussian noise
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