However, with the increase in model order by one for a given signal, only the value of the last (highest order) reflection coefficient will be changed.
In this paper, we have extended these methods to account for an unknown number of harmonics, a quantity also known as the model order, by deriving a model-specific order estimation criterion based on the maximum a posteriori principle.
Our attempt to determine the optimal model order by locating the minimum of the AIC and SBC as a function of model order (p investigated between 2 30) revealed no consistent solution.
We opted for this way, after we had tried to find the optimal model order by the use of the Akaike Information Criterion, AIC [80] or of the Schwarz's Bayesian Criterion, SBC [81].
To compare the overlap between gene rankings based on the ratio and intensity models, genes were ordered by p-value and assigned to bins containing 1,000 genes.
The fitted models were then rank ordered by their AICs [ 24].
Another possibility would be to reduce the complexity of the model by simplifying its model structure, e.g., by lowering the model order, or by aggregating some parameters.
We consider model order reduction by proper orthogonal decomposition (POD) for parametrized partial differential equations, where the underlying snapshots are computed with adaptive finite elements.
The usage of model order reduction by means of modal truncation leads to a design system of feasible dimension and also directly represents a suitable form for the determination of the flatness-based state and input parametrization.
Followed by the work of [6], a DFT-based approximation of polynomial SVD is also proposed in [18] which uses model order truncation by phase optimization.
One might select the model order K by arg max p(K| x, y) with K ∈{0,1,…, Kmax} and also can perform parameter estimation by computing the conditional expectation E(θ K | x, y) based on (10) shown subsequently.
