Invariance properties of the likelihood ratio for covariance matrix estimation in some complex elliptically contoured distributions

• We consider a class of complex elliptically contoured matrix distributions (ECD).
• We investigate properties of the likelihood ratio (LR).
• We derive stochastic representations of the LR for covariance matrix estimation (CME).
• Its p.d.f. evaluated at the true CM R0 does not depend on the latter.
• This extends the expected likelihood approach for regularized CME.

The likelihood ratio (LR) for testing if the covariance matrix of the observation matrix X is R has some invariance properties that can be exploited for covariance matrix estimation purposes. More precisely, it was shown in Abramovich et al. (2004, 2007, 2007) that, in the Gaussian case, LR(R0|X), where R0 stands for the true covariance matrix of the observations X, has a distribution which does not depend on R0 but only on known parameters. This paved the way to the expected likelihood (EL) approach, which aims at assessing and possibly enhancing the quality of any covariance matrix estimate (CME) by comparing its LR to that of R0. Such invariance properties of LR(R0|X) were recently proven for a class of elliptically contoured distributions (ECD) in Abramovich and Besson (2013) and Besson and Abramovich (2013) where regularized CME were also presented. The aim of this paper is to derive the distribution of LR(R0|X) for other classes of ECD not covered yet, so as to make the EL approach feasible for a larger class of distributions.