Generalized Variance in high dimension setting (p>n) Suppose I have $n$ data vector $X_1, ..., X_n$, where each $X_i$ is a length $p$ random vector.
The sample covariance matrix is $S=\frac{1}{n-1}\sum_i (X_i-\bar{X})(X_i-\bar{X})^T$. If I want to compute the generalized variance, I can compute the determinant of $S$, i.e. $|S|$.
In my actual use case where $p$ is fixed, but $p>n$, is $|S|$ the best estimator that I can use?
In my literature search I come across two types of papers, one that address the case when $p<n$ and talks about several different kinds of better estimators. The other talks about the case when $p$ can grow, but $p(n)<n$, and they talk about asymptotic results. This later case confuses me a bit because in my case, since $p$ is fixed, $n$ will become larger than $p$ in the asymptotics, but in practice I will never be in that case.
Edit
My purpose is to use the generalized variance as summary statistics. I know using a one number summary to describe the variability of multivariate data (functional data actually, in my case) is not a brilliant idea. But that is what is needed for practical purpose. So $\hat{\Sigma}$ (whatever estimator we use) has $p\times p$ many numbers (or $pC2$ many), which is too much. A one number summary like $tr(\hat{\Sigma})$ or $|\hat{\Sigma}|$ it better. 
 A: The problem with $p \geq n$
If $p \geq n$ then as you recognize your covariance matrix is necessarily rank deficient. 
Consider $p=2$ case. If  $n=3$ you can find the ellipse defined by the estimated covariance matrix for the three points.
 
But if $n=2$, then what is the ellipse supposed to be?! The rank deficient covariance matrix defines the degenerate ellipse that's a line segment between the two points. For $p \geq n$, the usual sample covariance has some dimension with zero variation (in this case, basically the $(1,-1)$ vector). Does that mean there's actually zero variation on that dimension, that your data only occupies a one dimensional space? (Or if we drew a third blue point, could it fall within one of the larger ellipses?)
If you're a Bayesian, what's your prior on the covariance matrix? A direction perhaps to go is to add some kind of prior or regularization? (I'm admittedly not an expert in this area. I'd love to see other answers.)
And I think we'll have to know more about your problem and why you want the determinant to have an idea of how to proceed? Perhaps you want something like the pseudo-determinant?
