A seminar by Dr Ziyang Lyu from UNSW
Title: On the inverse of covariance matrices for unbalanced crossed designs
Abstract: This presentation addresses a long-standing open problem in the analysis of linear mixed models with crossed random effects under unbalanced designs: how to find an analytic expression for the inverse of $\bV$, the covariance matrix of the observed response. The inverse matrix $\bV^{-1}$ is required for likelihood-based estimation and inference. However, for unbalanced crossed designs, $\bV$ is dense and the lack of a closed-form representation for $\bV^{-1}$, until now, has made using likelihood-based methods computationally challenging and difficult to analyse mathematically. We use the Khatri--Rao product to represent $\bV$ and then to construct a modified covariance matrix whose inverse admits an exact spectral decomposition. Building on this construction, we obtain an elegant and simple approximation to $\bV^{-1}$ for asymptotic unbalanced designs.
For non-asymptotic settings, we derive an accurate and interpretable approximation under mildly unbalanced data and establish an exact inverse representation as a low-rank correction to this approximation, applicable to arbitrary degrees of unbalance.
Simulation studies demonstrate the accuracy, stability, and computational tractability of the proposed framework.
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