On the maximum likelihood method of identification
IBM Journal of Research and Development
Information science: On the choice of sampling rates in parametric identification of time series
Information Sciences: an International Journal
Brief paper: Model approximations via prediction error identification
Automatica (Journal of IFAC)
Paper: Maximum-power validation of models without higher-order fitting
Automatica (Journal of IFAC)
Comparison of different methods for identification of industrial processes
Automatica (Journal of IFAC)
On the problem of ambiguities in maximum likelihood identification
Automatica (Journal of IFAC)
Maximum likelihood estimation of parameters in multivariate Gaussian stochastic processes (Corresp.)
IEEE Transactions on Information Theory
On efficient parametric identification methods for linear discrete stochastic systems
Automation and Remote Control
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The basic ideas behind the parameter estimation methods are discussed in a general setting. The application to estimation or parameters in dynamical systems is treated in detail using the prototype problem of estimating parameters in a continuous time system using discrete time measurements. Computational aspects are discussed. Theoretical results in consistency, asymptotic normality and efficiency are covered. Model validation and selection of model structures are discussed. An example is given which illustrates some properties of the methods and shows the usefulness of interactive computing. Additional examples illustrate what happens when the data has different artefacts.