On optimal adaptive prediction of multivariate ARMA(1,1) process | Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitelnaja tehnika i informatika – Tomsk State University Journal of Control and Computer Science. 2015. № 1(30).

On optimal adaptive prediction of multivariate ARMA(1,1) process

The problem of asymptotic efficiency of adaptive one-step predictors for ARMA(1,1) process with unknown dynamic parameters is considered. The predictors are based on the truncated estimators of the unknown matrix. The truncated estimation method is a modification of the truncated sequential estimation method, that yields estimators with a given accuracy by samples of fixed size. The criterion of prediction optimality is based on the loss function, defined as a linear combination of sample size and squared prediction error's sample mean. The cases of known and unknown variance of the noise model are studied. In the latter case the optimal sample size is a special stopping time.

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Keywords

адаптивные прогнозы, асимптотическая риск-эффекитвность, многомерный АРМА, момент остановки, оптимальный размер выборки, усечённое оценивание, adaptive predictors, asymptotic risk efficiency, multivariate ARMA, optimal sample size, stopping time, truncated parameter estimators

Authors

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

Всего: 1

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