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Functions f depend on the particular estimation framework, on the type of estimator and on the available information [21].
One can choose the type of estimator for the computation, for example, maximum likelihood (ML) estimation or Bayesian estimation.
This paper deals with a new type of estimator for discrete-time linear systems with unknown inputs.
However, the results are not robust to the type of estimator we use.
Hence, it is not a matter of the use of age with a specific type of estimator.
This is followed by two real data examples used by previous researchers in the past to illustrate this type of estimator.
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It exploits the underlying relation among adjacent MDCT values and provides a general form of this type of estimators.
This type of estimators is based on the idea that in the presence of structural breaks, older observations should be discounted more than more recent ones.
However since M-estimators are simple to understand and considering that recent methods are sometimes so sensitive that they wrongly identify good points as outliers (Hund et al. 2002), we chose to use these type of estimators.
For the former step, BayesB type of estimators have been proposed, which assume a priori that many markers have no effects, and some have an effect coming from a gamma or exponential distribution, i.e. a fat-tailed distribution.
Both types of estimator can be extended to use more than the first closest individual and in angle-order methods these measurements are repeated for each sector.
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