The designs which protect against model misspecification are clusters of observations about the points that would have been the design points for classical variance-minimizing designs.
Setting p = 2 results in the classical minimum variance LP analysis problem.
One consequence of this is that the classical epistatic variance components, which do not distinguish directional and non-directional effects, are useless as predictors of evolutionary dynamics.
Simulations show that the array gain of the proposed beamformer exceeds that of the classical minimum variance beamformer for a finite number of samples and coherent interference scenario, using the sample matrix inverse technique or the diagonal loading approach.
However, it is shown in [1] that even when the vocal tract filter follows an actual all-pole model, this criterion of goodness is not fulfilled by the classical minimum variance predictor.
Also, to show the usefulness of such sparse representation, we use the resulting prediction coefficients inside a multi-pulse excitation (MPE) coder and we show that the corresponding multi-pulse excitation source provides slightly better synthesis quality compared to the estimated excitation of the classical minimum variance synthesizer.
To further describe the classical minimum variance investment opportunity set for the first period, we consider the problem begin{array}rcl@ v^{ast} &=&min_{a}a^{prime}Sigma a s.t.~a^{prime}mu &=&m a^{prime }theta &=&d a^{prime }mathbf{1} &=&1.
The solution to the l1-norm minimization is not as easy as the classical minimum variance LP analysis problem but it can be solved by recasting the minimization problem into a linear program [12] and then using convex optimization tools [5].
More sophisticated choices for the shapes of the spatial regions upon which making our local estimates, instead of square blocks, should improve the classical bias-variance trade-off (bigger regions produce higher bias and lower variance, and vice versa) present at any estimation problem on non-stationary random fields, as pointed out in the conclusions.
As a consequence, we observe that the classical mean-variance frontier for a single-period is now three-dimensional.
