Estimation of the parametric regression with a pulse noiseby discrete time observations
The paper considers the problem ofparametric estimation in a continuous time linear parametric regression model with a non-Gaussian Ornstein - Uhlenbeck process by discrete time observations. Improved estimates withsmaller mean square risk as compared with the ordinary least square estimates are proposed forthe unknown regression parameters. The asymptotic minimaxity of these estimates in the sense ofthe robust risk has been proved.
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Keywords
негауссовская параметрическая регрессия, улучшенное оценивание, метод наименьших квадратов, импульсный шум, процесс Орнштейна - Уленбека, квадратический риск, минимаксность, non-Gaussian parametric regression, improved estimation, least square estimates, pulse noise, Ornstein - Uhlenbeck process, quadratic risk, minimaxityAuthors
| Name | Organization | |
| Konev Victor Vasilevich | National Research Tomsk State University | vvkonev@mail.tsu.ru |
| Pchelintsev Evgeny Anatolevich | National Research Tomsk State University; Université de Rouen (France) | evgen-pch@yandex.ru |
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