Spiked multiplicative random matrices and principal components

Xiucai Ding and Hong Chang Ji

Stochastic Processes and their Applications, 2023, vol. 163, issue C, 25-60

Abstract: In this paper, we study the eigenvalues and eigenvectors of the spiked invariant multiplicative models when the randomness is from Haar matrices. We establish the limits of the outlier eigenvalues λ̂i and the generalized components (〈v,ûi〉 for any deterministic vector v) of the outlier eigenvectors ûi with optimal convergence rates. Moreover, we prove that the non-outlier eigenvalues stick with those of the unspiked matrices and the non-outlier eigenvectors are delocalized. The results also hold near the so-called BBP transition and for degenerate spikes. On one hand, our results can be regarded as a refinement of the counterparts of Belinschi et al. (2017) under additional regularity conditions. On the other hand, they can be viewed as an analog of Ding and Yang (2021) by replacing the random matrix with i.i.d. entries with Haar random matrix.

Keywords: Free multiplication of random matrices; Spiked model; Principal components; Local laws (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.spa.2023.05.009

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