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

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Abstract

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 × q n upper left block of a random matrix Γ 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 nq n independent standard normals goes to zero provided p n = o(√/n) and q n = o(√/n). We also show that the above variation distance does not go to zero if p n = [x√/n] and q n = [y√/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 Γ n = (γij)n×n is generated by performing the GramSchmidt algorithm on the columns of Y n = (yij)n×n, where {yij; 1 ≤ i, j ≤ n} are i.i.d. standard normals. We show that ε n(m):=max 1≤i≤n,1≤j≤m |√n· γij - yij | goes to zero in probability as long as m = m n = o(n/ log n). We also prove that ε n (m n) → 2√α in probability when m n = [nα/ log n] for any α > 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 Γ n can be approximated simultaneously by independent standard normals.

Original languageEnglish (US)
Pages (from-to)1497-1529
Number of pages33
JournalAnnals of Probability
Volume34
Issue number4
DOIs
StatePublished - Jul 2006

Keywords

  • Gram-Schmidt algorithm
  • Haar measure
  • Large deviation
  • Maxima
  • Product distribution
  • Random matrix theory
  • Variation distance

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