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Under the diagonal covariance restriction, the profile log-likelihood separates into $p$ terms,Differentiating each term gives the restricted maximum-likelihood estimator ${\hat{V}}_0=D=\mathrm{diag}(S_{11},\dots ,S_{pp})$. Both fitted trace terms equal $p$, so the likelihood-ratio test statistic iswhere $R=D^{-1/2}S{D}^{-1/2}$ is the sample correlation matrix. The statistic is nonnegative by the Hadamard inequality; large values indicate dependence. Equivalently, the likelihood ratio is $(\det S/\det D{)}^{n/2}$ and rejection is for small values of this ratio.

For fixed $p$ and a positive-definite true covariance, the Wilks theorem says that twice the maximized log-likelihood difference for nested regular models converges under the null to a chi-squared distribution with degrees of freedom equal to the dimension difference. The unrestricted covariance has $p(p+1)/2$ parameters and the restricted covariance has $p$; the unknown mean has the same $p$ parameters in both. Therefore the Gaussian covariance diagonality likelihood-ratio test of asymptotic level $\alpha$ isWithin the multivariate normal distribution, diagonal covariance also means independent coordinates.