Risk efficiency of adaptive one-step prediction of autoregression with parameter drift | Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitelnaja tehnika i informatika – Tomsk State University Journal of Control and Computer Science. 2015. № 3(32).

Risk efficiency of adaptive one-step prediction of autoregression with parameter drift

A scalar stable autoregressive process with the dynamic parameter corrupted by additive noise is studied. The model parameters are assumed to be unknown. The truncated estimators of the parameters are used to build adaptive one-step predictors. The associated problem is to minimize a risk function of special form, defined to account for sample size and squared prediction error's sample mean. A sequential procedure is introduced to achieve the minimal risk.

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Keywords

adaptive predictors, asymptotic risk efficiency, optimal sample size, scalar autoregression, stopping time, truncated parameter estimators, адаптивные прогнозы, асимптотическая риск-эффективность, оптимальный размер выборки, скалярная авторегрессия, момент остановки, усечённое оценивание

Authors

Name	Organization	E-mail
Kusainov Marat I.	Tomsk State University	rjrltsk@gmail.com

Всего: 1

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