Dear Aziz -
The approximate likelihood methods in NONMEM such as FO, FOCE,and  LAPLACE  
optimize an objective function than is parameterized internally
by the Cholesky factor L of Omega, regardless of whether the matrix is diagonal 
(the EM -based methods do something considerably different and work directly 
with Omega rather than
the Cholesky factor.)
 
Thus for the approximate likelihood methods, the SE's computed internally by 
$COV from the Hessian or Sandwich or Fisher score methods 
are first computed with respect to these Cholesky parameters , and then the 
corresponding SE's of the full Omega=LL' are computed by a 'propagation of 
errors' approach
which skews the results, particularly if the SE's are large.  Thus in a sense 
regarding your dilemma about whether Model 1 or Model 2 is better with respect 
to applicability of $COV results, one answer is that both are fundamentally 
distorted  by the propagation of errors method with respect to the Omega 
elements.

But regarding  your fundamental question 'can we trust the output of $COV '- 
all of this makes very little difference.  Standard errors computed by $COV are 
inherently dubious - the applicability of the usual asymptotic arguments is 
very questionable for the types/sizes of data sets we often deal with.
As Lewis Sheiner used to say of these results, 'they are not worth the 
electrons used to compute them'.    They are the best we can do for the level 
of computational investment put into them -
If you want something better, try a bootstrap or profiling method.



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