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Recent advances in the spectral theory of signal-plus-noise type matrices provide a unified framework for characterizing limiting spectral distributions, extreme eigenvalues, and linear spectral statistics, which are highly applicable to high-dimensional inference, signal processing, and machine learning. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Signal Plus Noise Type Matrix in Random Matrix Theory: A Review

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