Researchers have introduced MOMENT, a novel framework for selecting and estimating parameters in multiresponse linear mixed-effects models. This stage-wise approach leverages second-order cross-moment identities to efficiently determine the random-effects covariance matrix and fixed-effects coefficients. The method induces sparsity under a positive semidefinite constraint, transforming the selection problem into a solvable convex optimization task. Theoretical guarantees for the procedure include random-effects and fixed-effects selection consistency, with simulations demonstrating its competitiveness and effectiveness in handling correlated responses, as shown in an application to a hemodialysis dataset. AI
RANK_REASON The cluster contains a single academic paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]
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