How many entries of a typical orthogonal matrix can be approximated by independent normals?

• 成果类型: Article
• 署名作者: Jiang, Tiefeng
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: ANNALS OF PROBABILITY
• ISSN/ISSBN: 0091-1798
• DOI: 10.1214/009117906000000205
• 发表日期: 2006
• 页码: 1497-1529
• 关键词: sample covariance-matrix; LIMIT-THEOREMS; eigenvalues

摘要

We solve an open problem of Diaconis that asks what are the largest orders of p(n) and q(n) such that Z(n), the p(n) x q(n) upper left block of a random matrix Gamma(n) which is uniformly distributed on the orthogonal group O(n), can be approximated by independent standard normals? This problem is solved by two different approximation methods. First, we show that the variation distance between the joint distribution of entries of Z(n) and that of p(n)q(n) independent standard normals goes to zero provided p(n) = o(root n) and q(n) = o(root n). We also show that the above variation distance does not go to zero if p(n) = [x root n] and q(n) = [y root n] for any positive numbers x and y. This says that the largest orders of p(n) and q(n) are o(n(1/2)) in the sense of the above approximation. Second, suppose Gamma(n) = (y(ij))(nxn) is generated by performing the Gram-Schmidt algorithm on the columns of Y-n = (y(ij))(nxn), where {y(ij); 1 <= i, j <= n} are i.i.d. standard normals. We show that epsilon(n)(m) : = max(1 <= i <= n,1 <= j <= m)vertical bar root n center dot y(ij) - y(ij)vertical bar goes to zero in probability as long as m = m(n) = o(n/log n). We also prove that epsilon(n) (m(n)) -> 2 root alpha in probability when m(n) = [n alpha/log n] for any alpha > 0. This says that m(n) = o(n/log n) is the largest order such that the entries of the first m(n) columns of Gamma(n) can be approximated simultaneously by independent standard normals.