Robust precoding that takes the channel inaccuracy into account is an important safeguard against remaining cases with problematic channel matrices.
However, when n is of the order of tens of thousands, new algorithms are required because even storing the squared correlation matrix is problematic.
This is trivial when the output is a short assignment, like y = 1, but becomes problematic if a large matrix is created.
The introduction of a (N_{mathrm{task}}^{2}) sized matrix can become problematic when the number of MPI tasks is increased beyond the numbers we have tested for this publication, especially for supercomputers with relatively little available memory per core (Blue Gene architecture).
ANOVA can, in some cases, be a problematic test for non-independent matrix observations, but the inclusion of the region variable helps to overcome spatial dependence in the dataset.
However, adequate diffusion of active compounds through an aqueous agar matrix may be problematic, particularly in the case of non-polar extracts [ 29].
Since MLE is less problematic when analyzing the covariance matrix, and since Fouladi [ 52] found in a simulation study that the Bollen-Stine test of overall model fit performed well relative to other methods of testing model fit, we decided to estimate the model with MLE and additional Bollen-Stine bootstrapping.
While the identification of biomarkers directly in blood-based matrices can be problematic due to their complexity and the presence of multiple highly abundant factors [ 14], some of these challenges can be minimized through extensive fractionation [ 15].
Specific and rapid detection and quantification of mycobacteria in contaminated metalworking fluid (MWF) are problematic due to complexity of the matrix and heavy background co-occurring microflora.
