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Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects
Mixed-effects models are widely used to model data with complex grouping structures and high-cardinality categorical predictor variables. However, for high-dimensional crossed random effects, current standard computations relying on Cholesky decompositions can become prohibitively slow. In this work
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