In this paper the identification problem is considered for initial conditions in a non-minimal state-space model that includes interpretable state variables generated by non-stationary stochastic processes. In order to solve the identification problem, structural restrictions are imposed on initial conditions in a state-space model with redundant state variables. The corresponding restricted maximum likelihood estimator of initial conditions is derived. The restricted estimator of initial conditions can be used in order to compute uniquely identified realizations of interpretable latent variables. The identification problem is illustrated analytically using a simple structural economic mode
A new subspace algorithm consistently identifies stochastic state space models directly from given o...
Abstract: Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonl...
textabstractThis paper concerns the modelling of stochastic processes by means of dynamic factor mod...
This paper presents theory and algorithms for validation in system identification of state-space mod...
We consider the identification of a Markov process {W_t, X^(*)_t} when only {W_t} is observed. In s...
State-space modeling provides a powerful tool for system identification and prediction. In linear sta...
In this paper we consider identification of multivariable linear systems using state-space models. A...
The study deals with the identification and estimation of the unknown parameters of an ‘extended’ st...
This paper presents theory, algorithms, and validation results for system identification of continuo...
This paper presents theory and algorithms for validation in system identification of state-space mod...
State-space modeling provides a powerful tool for system identification and prediction. In linear st...
In this paper some aspects of subspace identification are studied. The focus is on those subspace me...
A dynamical process is modelled by a system of non-linearizable ordinary differential equations with...
This work focuses on the identification of nonlinear dynamic systems. In particular the problem of o...
This paper is concerned with the parameter estimation of a general class of nonlinear dynamic system...
A new subspace algorithm consistently identifies stochastic state space models directly from given o...
Abstract: Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonl...
textabstractThis paper concerns the modelling of stochastic processes by means of dynamic factor mod...
This paper presents theory and algorithms for validation in system identification of state-space mod...
We consider the identification of a Markov process {W_t, X^(*)_t} when only {W_t} is observed. In s...
State-space modeling provides a powerful tool for system identification and prediction. In linear sta...
In this paper we consider identification of multivariable linear systems using state-space models. A...
The study deals with the identification and estimation of the unknown parameters of an ‘extended’ st...
This paper presents theory, algorithms, and validation results for system identification of continuo...
This paper presents theory and algorithms for validation in system identification of state-space mod...
State-space modeling provides a powerful tool for system identification and prediction. In linear st...
In this paper some aspects of subspace identification are studied. The focus is on those subspace me...
A dynamical process is modelled by a system of non-linearizable ordinary differential equations with...
This work focuses on the identification of nonlinear dynamic systems. In particular the problem of o...
This paper is concerned with the parameter estimation of a general class of nonlinear dynamic system...
A new subspace algorithm consistently identifies stochastic state space models directly from given o...
Abstract: Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonl...
textabstractThis paper concerns the modelling of stochastic processes by means of dynamic factor mod...