  Maximum Likelihood (ML) estimation requires precise knowledge of the
underlying statistical model. In Quasi ML (QML), a presumed model is used as a
substitute to the (unknown) true model. In the context of Independent Vector
Analysis (IVA), we consider the Gaussian QML Estimate (QMLE) of the demixing
matrices set and present an (approximate) analysis of its asymptotic separation
performance. In Gaussian QML the sources are presumed to be Gaussian, with
covariance matrices specified by some "educated guess". The resulting
quasi-likelihood equations of the demixing matrices take a special form,
recently termed an extended "Sequentially Drilled" Joint Congruence (SeDJoCo)
transformation, which is reminiscent of (though essentially different from)
classical joint diagonalization. We show that asymptotically this QMLE, i.e.,
the solution of the resulting extended SeDJoCo transformation, attains perfect
separation (under some mild conditions) regardless of the sources' true
distributions and/or covariance matrices. In addition, based on the
"small-errors" assumption, we present a first-order perturbation analysis of
the extended SeDJoCo solution. Using the resulting closed-form expressions for
the errors in the solution matrices, we provide closed-form expressions for the
resulting Interference-to-Source Ratios (ISRs) for IVA. Moreover, we prove that
asymptotically the ISRs depend only on the sources' covariances, and not on
their specific distributions. As an immediate consequence of this result, we
provide an asymptotically attainable lower bound on the resulting ISRs. We also
present empirical results, corroborating our analytical derivations, of three
simulation experiments concerning two possible model errors - inaccurate
covariance matrices and sources' distribution mismodeling.
