Pavel Yaskov, “LLN for quadratic forms of long memory time series and its applications in random matrix theory”, Journal of Theoretical Probability, 2018, Volume 31, Issue 4, Pages 2032

This publication is cited in the following 8 articles:

Patrice Abry, Gustavo Didier, Oliver Orejola, Herwig Wendt, “On the empirical spectral distribution of large wavelet random matrices based on mixed-Gaussian fractional measurements in moderately high dimensions”, Electron. J. Probab., 30:none (2025)
P. A. Yaskov, “Sufficient conditions for the Marchenko–Pastur theorem”, Theory Probab. Appl., 68:4 (2024), 657–673
Yifu Tang, Claudia Kirch, Jeong Eun Lee, Renate Meyer, “Posterior consistency for the spectral density of non‐Gaussian stationary time series”, Scandinavian J Statistics, 50:3 (2023), 1152
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
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
Jennifer Bryson, Roman Vershynin, Hongkai Zhao, “Marchenko–Pastur law with relaxed independence conditions”, Random Matrices: Theory Appl., 10:04 (2021)
P. A. Yaskov, “On a spectrum of sample covariation matrices for time series”, Theory Probab. Appl., 62:3 (2018), 432–443