Seminar Series of the Department of Statistics and Data Science, No. 507
Maximum of Sparsely Equicorrelated Gaussian Fields and Applications
Time: Wednesday, April 1, 2026, 10:30-11:30 a.m.
Venue: Guoshun Campus, Starr Building, Room 403
Moderator: Professor Deyuan Li
Speaker: Professor Yongcheng Qi, Department of Mathematics and Statistics, University of Minnesota Duluth, USA
Title: Maximum of Sparsely Equicorrelated Gaussian Fields and Applications
Abstract: We investigate the extreme values of a sparse and equicorrelated Gaussian field on a triangle: the correlations on every vertical or horizontal line are all equal to a parameter r∈[0,1/2] and are zero everywhere else. This problem is closely linked with various problems in high-dimensional statistics and extreme-value theory. We identify the threshold for r at which the standard Gumbel law breaks down. Our result is based on a subtle application of the Chen-Stein method for Poisson approximation. As applications, we resolve several questions that were left open in Heiny and Kleemann (2025), Tang, Lu and Xie (2022), and Fan and Jiang (2019).
Bio: Dr. Yongcheng Qi is professor at Department of Mathematics and Statistics, University of Minnesota Duluth. He received a BS degree in Mathematics from Peking University in 1987 and PhD in Probability and Statistics in 1992. From 1994 to 1997, Dr Qi was an associate professor at Department of Probability and Statistics, Peking University. He obtained his PhD degree in Statistics from University of Georgia in 2001. His research interests include limit theorems in Probability and Statistics, empirical likelihood methods, high-dimensional statistics and theory of random matrices. Some of his papers were published in Annals of Statistics, Transactions of the American Mathematical Society, Journal of Theoretical Probability, Statistica Sinica, Scandinavian Journal of Statistics, Journal of Multivariate Analysis, Journal of Mathematical Physics, Insurance: Mathematics and Economics, Stochastic Processes and their Applications, Journal of Applied Probability, and Journal of Applied Statistics.