Add to ORKGAbstract: The problem of parameters estimation of an autoregressive process is considered. The method of guaranteed estimation is based on the least squares method with special weights and uses a special stopping rule. The properties of the procedures are studied for the case of known and unknown variance of the noise. Copyright c © 2002 IFA
This paper considers the problem of estimating the parameters of an autoregressive (AR) process in p...
AbstractFor a stable autoregressive process of order p with unknown vector parameter θ, it is shown ...
A procedure for assigning optimal weights to the prediction equations which are used to obtain the p...
Critical random coefficient AR(1) processes are investigated where the random coefficient is binary,...
A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals...
A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals...
For an autoregressive process of order p, the paper proposes new sequential estimates for the unknow...
The problem of estimating parameters of autoregressive (AR) signals from noisy data is studied in th...
For parameters in a threshold autoregressive process, the paper proposes a sequential modification o...
This paper considers the least squares estimation and establishes its asymptotic theory for threshol...
We derive a weighted least squares approximate restricted likelihood estimator for a k-dimensional p...
Estimation of autoregressive (AR) signals measured in white noise is considered. A well-known fact i...
The paper considers the problem of estimating the autoregressive parameter in the first-order autore...
We consider the problem of estimating the parameters of an autoregressive process based on observati...
AbstractThis paper establishes several almost sure asymptotic properties of general autoregressive p...
This paper considers the problem of estimating the parameters of an autoregressive (AR) process in p...
AbstractFor a stable autoregressive process of order p with unknown vector parameter θ, it is shown ...
A procedure for assigning optimal weights to the prediction equations which are used to obtain the p...
Critical random coefficient AR(1) processes are investigated where the random coefficient is binary,...
A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals...
A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals...
For an autoregressive process of order p, the paper proposes new sequential estimates for the unknow...
The problem of estimating parameters of autoregressive (AR) signals from noisy data is studied in th...
For parameters in a threshold autoregressive process, the paper proposes a sequential modification o...
This paper considers the least squares estimation and establishes its asymptotic theory for threshol...
We derive a weighted least squares approximate restricted likelihood estimator for a k-dimensional p...
Estimation of autoregressive (AR) signals measured in white noise is considered. A well-known fact i...
The paper considers the problem of estimating the autoregressive parameter in the first-order autore...
We consider the problem of estimating the parameters of an autoregressive process based on observati...
AbstractThis paper establishes several almost sure asymptotic properties of general autoregressive p...
This paper considers the problem of estimating the parameters of an autoregressive (AR) process in p...
AbstractFor a stable autoregressive process of order p with unknown vector parameter θ, it is shown ...
A procedure for assigning optimal weights to the prediction equations which are used to obtain the p...