AbstractThis paper presents a two-stage least squares based iterative algorithm, a residual based interactive least squares algorithm and a residual based recursive least squares algorithm for identifying controlled autoregressive moving average (C-ARMA) models. The simulation studies indicate that the proposed algorithms can effectively estimate the parameters of the C-ARMA models
We propose new estimation methods for time series models, possibly noncausal and/or noninvertible, u...
Stable autoregressive models of known finite order are considered with martingale differences errors...
We propose new estimation methods for time series models, possibly noncausal and/or noninvertible, u...
AbstractThis paper presents a two-stage least squares based iterative algorithm, a residual based in...
AbstractA two-stage least squares based iterative (two-stage LSI) identification algorithm is derive...
AbstractAn iterative least squares parameter estimation algorithm is developed for controlled moving...
A recursive algorithm for ARMA (autoregressive moving average) filtering has been developed in a com...
AbstractOne computationally efficient procedure for obtaining maximum likelihood parameter estimates...
Usually the coefficients in a stochastic time series model are partially or entirely unknown when th...
We are interested in the implications of a linearly autocorrelated driven noise on the asymptotic be...
summary:In this paper, we consider the parameter estimation problem for the multivariable system. A ...
This paper introduces an estimator for errors-in-variables models in which all measurements are corr...
This study is based on the observation that if the bootstrapping is combined with different paramete...
This paper concentrates on the recursive identification algorithms for the exponential autoregressiv...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57826/1/JunBernsteinErrorsinVariablesIJ...
We propose new estimation methods for time series models, possibly noncausal and/or noninvertible, u...
Stable autoregressive models of known finite order are considered with martingale differences errors...
We propose new estimation methods for time series models, possibly noncausal and/or noninvertible, u...
AbstractThis paper presents a two-stage least squares based iterative algorithm, a residual based in...
AbstractA two-stage least squares based iterative (two-stage LSI) identification algorithm is derive...
AbstractAn iterative least squares parameter estimation algorithm is developed for controlled moving...
A recursive algorithm for ARMA (autoregressive moving average) filtering has been developed in a com...
AbstractOne computationally efficient procedure for obtaining maximum likelihood parameter estimates...
Usually the coefficients in a stochastic time series model are partially or entirely unknown when th...
We are interested in the implications of a linearly autocorrelated driven noise on the asymptotic be...
summary:In this paper, we consider the parameter estimation problem for the multivariable system. A ...
This paper introduces an estimator for errors-in-variables models in which all measurements are corr...
This study is based on the observation that if the bootstrapping is combined with different paramete...
This paper concentrates on the recursive identification algorithms for the exponential autoregressiv...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57826/1/JunBernsteinErrorsinVariablesIJ...
We propose new estimation methods for time series models, possibly noncausal and/or noninvertible, u...
Stable autoregressive models of known finite order are considered with martingale differences errors...
We propose new estimation methods for time series models, possibly noncausal and/or noninvertible, u...