73 research outputs found
Linear System Identification - A Survey
In this paper we give an introductory survey on the theory of identification of (in general MIMO) linear systems from (discrete) time series data. The main parts are: Structure theory for linear systems, asymptotic properties of maximum likelihood type estimators, estimation of the dynamic specification by methods based on information criteria and finally, extensions and alternative approaches such as identification of unstable systems and errors-in-variables
Quasi maximum likelihood estimation for strongly mixing state space models and multivariate L\'evy-driven CARMA processes
We consider quasi maximum likelihood (QML) estimation for general
non-Gaussian discrete-ime linear state space models and equidistantly observed
multivariate L\'evy-driven continuoustime autoregressive moving average
(MCARMA) processes. In the discrete-time setting, we prove strong consistency
and asymptotic normality of the QML estimator under standard moment assumptions
and a strong-mixing condition on the output process of the state space model.
In the second part of the paper, we investigate probabilistic and analytical
properties of equidistantly sampled continuous-time state space models and
apply our results from the discrete-time setting to derive the asymptotic
properties of the QML estimator of discretely recorded MCARMA processes. Under
natural identifiability conditions, the estimators are again consistent and
asymptotically normally distributed for any sampling frequency. We also
demonstrate the practical applicability of our method through a simulation
study and a data example from econometrics
Gibbs Sampling, Exponential Families and Orthogonal Polynomials
We give families of examples where sharp rates of convergence to stationarity
of the widely used Gibbs sampler are available. The examples involve standard
exponential families and their conjugate priors. In each case, the transition
operator is explicitly diagonalizable with classical orthogonal polynomials as
eigenfunctions.Comment: This paper commented in: [arXiv:0808.3855], [arXiv:0808.3856],
[arXiv:0808.3859], [arXiv:0808.3861]. Rejoinder in [arXiv:0808.3864].
Published in at http://dx.doi.org/10.1214/07-STS252 the Statistical Science
(http://www.imstat.org/sts/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Indirect Adaptive Attenuation of Multiple Narrow-Band Disturbances Applied to Active Vibration Control
International audienceIn this brief, an indirect adaptive control methodology for attenuation of multiple unknown time varying narrow-band disturbances is proposed. This method is based on the real time estimation of the frequency of narrow-band disturbances using adaptive notch filters followed by the design of a controller using adjustable band-stop filters for the appropriate shaping of the output sensitivity function. A Youla-Kučera parametrization of the controller is used for reducing the computation load. This approach is compared on an active vibration control system with the direct adaptive control scheme based on the internal model principle proposed. Real time experimental results are provided
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