AbstractThis note contains a characterization of predictors for nonstationary ARMA processes. Moreover, we give the weak law of large numbers for those processes
This paper analyzes the effect of overdifferencing a stationary AR(p + 1) process whose largest root...
We introduce a two-step procedure for more efficient nonparametric prediction of a strictly station...
AbstractIn this paper we prove that the sum of two independent multivariate ARMA processes is also A...
AbstractThis note contains a characterization of predictors for nonstationary ARMA processes. Moreov...
This note contains a characterization of predictors for nonstationary ARMA processes. Moreover, we g...
AbstractThis paper introduces a concept of a general ARMA model. The Wold's decomposition is extende...
AbstractIn order to predict unobserved values of a linear process with infinite variance, we introdu...
summary:Let $\{W_t\}=\{(X'_{t'}, Y'_t)'\}$ be vector ARMA $(m,n)$ processes. Denote by $\hat{X}_t(a)...
In the time series analysis one of the most used predicting models are of so called auto regressive ...
The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem ...
AbstractApproximate L2-optimal predictor and filter is derived for partially observed vector autoreg...
AbstractThe aim of this note is to study the properties of some nonstationary autoregressive-moving ...
We construct prediction intervals for the observations of first-order autoregressive processes when ...
This paper gives a discussion on a misspecified ARMA(1,1) model fitting to an AR(2) process. The pro...
We develop likelihood-based tests for autocorrelation and predictability in a first order non- Gauss...
This paper analyzes the effect of overdifferencing a stationary AR(p + 1) process whose largest root...
We introduce a two-step procedure for more efficient nonparametric prediction of a strictly station...
AbstractIn this paper we prove that the sum of two independent multivariate ARMA processes is also A...
AbstractThis note contains a characterization of predictors for nonstationary ARMA processes. Moreov...
This note contains a characterization of predictors for nonstationary ARMA processes. Moreover, we g...
AbstractThis paper introduces a concept of a general ARMA model. The Wold's decomposition is extende...
AbstractIn order to predict unobserved values of a linear process with infinite variance, we introdu...
summary:Let $\{W_t\}=\{(X'_{t'}, Y'_t)'\}$ be vector ARMA $(m,n)$ processes. Denote by $\hat{X}_t(a)...
In the time series analysis one of the most used predicting models are of so called auto regressive ...
The Kalman filter is the celebrated algorithm giving a recursive solution of the prediction problem ...
AbstractApproximate L2-optimal predictor and filter is derived for partially observed vector autoreg...
AbstractThe aim of this note is to study the properties of some nonstationary autoregressive-moving ...
We construct prediction intervals for the observations of first-order autoregressive processes when ...
This paper gives a discussion on a misspecified ARMA(1,1) model fitting to an AR(2) process. The pro...
We develop likelihood-based tests for autocorrelation and predictability in a first order non- Gauss...
This paper analyzes the effect of overdifferencing a stationary AR(p + 1) process whose largest root...
We introduce a two-step procedure for more efficient nonparametric prediction of a strictly station...
AbstractIn this paper we prove that the sum of two independent multivariate ARMA processes is also A...
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