ASYMPTOTIC DISTRIBUTIONS OF LARGEST PEARSON CORRELATION COEFFICIENTS UNDER DEPENDENT STRUCTURES

• 成果类型: Article
• 署名作者: Jiang, Tiefeng; Pham, Tuan
• 署名单位: The Chinese University of Hong Kong, Shenzhen; University of Texas System; University of Texas Austin
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/24-AOS2462
• 发表日期: 2025
• 页码: 907-928
• 关键词: sample correlation-matrices; COVARIANCE-MATRIX; largest entries; limiting distributions; INDEPENDENCE; coherence; CONVERGENCE; eigenvalues; THEOREM

摘要

Given a random sample from a multivariate normal distribution whose covariance matrix is a Toeplitz matrix, we study the largest off-diagonal entry of the sample correlation matrix. Assuming the multivariate normal distribution has the covariance structure of an autoregressive sequence, we establish a phase transition in the limiting distribution of the largest off-diagonal entry. We show that the limiting distributions are of Gumbel-type (with different parameters), depending on how large or small the parameter of the autoregressive sequence is. At the critical case, we obtain that the limiting distribution is the maximum of two independent random variables of Gumbel distributions. This phase transition establishes the exact threshold at which the autoregressive covariance structure behaves differently than its counterpart with the covariance matrix equal to the identity. Assuming the covariance matrix is a general Toeplitz matrix, we obtain the limiting distribution of the largest entry under the ultrahigh-dimensional settings: it is a weighted sum of two independent random variables, one normal and the other following a Gumbel-type law. The counterpart of the non-Gaussian case is also discussed. We use our results to obtain new testing procedures for some problems in high-dimensional statistics.