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Briefly, estimation can 'pick up' a wrong subset of factors.
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Meyer and Kirkpatrick [ 19] noticed that selecting too low a rank in the direct PC approach can lead to picking up the wrong subset of PC, which can result in biased estimates.
Furthermore, with more than one matrix to be estimated, the reduced rank estimator can be inconsistent, i.e. pick up the wrong subset of PC [ 2].
The advantage of the bottom-up approach is that we estimate r elements of r × r D r-matrix in the parameter estimation step and therefore, there is no danger of picking up the wrong subset of principal components.
A more recent study of Meyer and Kirkpatrick [ 19] has listed three sources of bias of reduced rank estimates: spread of sample roots, constraining estimates to the parameter space and picking up the wrong subset of the genetic PC, if too few PC are fitted.
Similarly, a wrong order will be obtained for shared markers that belong to poorly overlapping subsets of mapping populations, if the missing experimental information on recombination rates between markers from these subsets is "derived" analytically based on linear approximation.
A wrong number?
A wrong turn.
Diodes conduct in a wrong way.
It causes a wrong B parameter.
In fact, they recover a wrong secret.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com