glasso- Assumptions of Meinhausen-Buhlmann approximation? First off, This is not an R question - it is a conceptual question, so there is no need to perform any code.
Problem:
I'm trying to invert a large dimensional covariance matrix of p features. For large p, even using a graphical LASSO with a regularization parameter, inverting this matrix takes a long time. In the past, I have elected instead to use the Meinhausen-Buhlmann approximation to speed the inversion up, using R's glasso() method from the glasso package. You can specify the approximation by setting the parameter approx to TRUE: glasso(cov.matrix, rho=0.15, approx=TRUE). This makes the inversion of even a large covariance matrix, very speedy.
However, I'm looking at my resulting graphical structure and I worry I don't trust the approximation, because the clustering of the nodes isn't very organized/interpretable. I expect to see clusters based on annotation groups each node belongs to.
Do I have reason to distrust the Meinhausen-Bulhmann approximation? What assumptions does it make? The original paper is a bit out of my depth, so I would appreciate help in digesting how the approximation works from experts/smarter people.
Thanks for any insights!
:-)
 A: Estimation quality of a precision matrix in high dimensions $p>>n$ through any procedure is dependent on assumptions--in short there's no free lunch, and I rather doubt there is a useful estimator that is minimax in the space of all precision matrices. 
In the case of the graphical lasso, the assumption is sparsity.  If your precision matrix is not sparse (say it was low-rank, instead), then the graphical lasso or neighborhood selection is unlikely to produce anything sensible.
Now, supposing your precision is sparse, and your data are indeed Gaussian: the theory for Meinshausen-Buhlmann (also known as neighborhood selection) merely relies on observing that the conditional distribution of each variable, conditioned on all others implies a linear regression relationship.  This follows from the properties of the conditional distribution of a multivariate normal distribution.
So the conditional, neighborhood selection procedure and the graphical lasso are estimating the same parameter.  I will try to dig up the reference, but in practice they are pretty similar in efficiency in finite samples as well.  So I would suggest that if you aren't happy with the networks you are seeing from neighborhood selection, the Gaussian model is probably mis-specified in some sense, rather than statistical inefficiency.  What do the marginal and bi-variate distributions look like?  Are they plausibly Gaussian?
