Wangjun Yuan, “On spectrum of sample covariance matrices from large tensor vectors”, ALEA, 21:2 (2024), 1527
P. A. Yaskov, “Ob asimptotike spektra sluchainykh matrits s nezavisimymi elementami”, UMN, 79:5(479) (2024), 181–182
P. A. Yaskov, “Sufficient conditions for the Marchenko–Pastur theorem”, Theory Probab. Appl., 68:4 (2024), 657–673
Pavel Yaskov, “Marchenko–Pastur law for a random tensor model”, Electron. Commun. Probab., 28 (2023), 23–17
P. A. Yaskov, “Limiting spectral distribution for large sample covariance matrices with graph-dependent elements”, Theory Probab. Appl., 67:3 (2022), 375–388
Benoît Collins, Jianfeng Yao, Wangjun Yuan, “On spectral distribution of sample covariance matrices from large dimensional and large k-fold tensor products”, Electron. J. Probab., 27:none (2022)
Pavel Yaskov, “Limit of the smallest eigenvalue of a sample covariance matrix for spherical and related distributions”, Springer Proc. Math. Statist., 371 (2021), 229–241
G. L. Zitelli, “Random matrix models for datasets with fixed time horizons”, Quantitative Finance, 20:5 (2020), 769
M Kornyik, “A note on the asymptotics of random density matrices”, J. Phys. Commun., 2:4 (2018), 045012
Pavel Yaskov, “LLN for quadratic forms of long memory time series and its applications in random matrix theory”, J. Theor. Probability, 31:4 (2018), 2032–2055