Premium
Rank 1 real Wishart spiked model
Author(s) -
Mo M. Y.
Publication year - 2012
Publication title -
communications on pure and applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.12
H-Index - 115
eISSN - 1097-0312
pISSN - 0010-3640
DOI - 10.1002/cpa.21415
Subject(s) - wishart distribution , eigenvalues and eigenvectors , mathematics , rank (graph theory) , distribution (mathematics) , combinatorics , matrix (chemical analysis) , random matrix , fredholm determinant , mathematical analysis , pure mathematics , statistics , physics , quantum mechanics , materials science , multivariate statistics , composite material
In this paper, we consider N ‐dimensional real Wishart matrices Y in the class \input amssym $W_{\Bbb R} (\Sigma ,M)$ in which all but one eigenvalue of Σ is 1. Let the nontrivial eigenvalue of Σ be 1+τ; then as N, M → ∞, with M / N → γ 2 finite and nonzero, the eigenvalue distribution of Y will converge into the Marchenko‐Pastur distribution inside a bulk region. When τ increases from 0, one starts to see a stray eigenvalue of Y outside of the support of the Marchenko‐Pastur density. As this stray eigenvalue leaves the bulk region, a phase transition will occur in the largest eigenvalue distribution of the Wishart matrix. In this paper we will compute the asymptotics of the largest eigenvalue distribution when the phase transition occurs. We will first establish the results that are valid for all N and M and will use them to carry out the asymptotic analysis. In particular, we have derived a contour integral formula for the Harish‐Chandra Itzykson‐Zuber integral $\int_{O(N)} {e^{{\rm tr}(XgYg^{\rm T} )} } g^{\rm T} dg$ when X and Y are real symmetric and Y is a rank 1 matrix. This allows us to write down a Fredholm determinant formula for the largest eigenvalue distribution and analyze it using orthogonal polynomial techniques. As a result, we obtain an integral formula for the largest eigenvalue distribution in the large‐ N limit characterized by Painlevé transcendents. The approach used in this paper is very different from a recent paper by Bloemenal and Virág, in which the largest eigenvalue distribution was obtained using a stochastic operator method. In particular, the Painlevé formula for the largest eigenvalue distribution obtained in this paper is new. © 2012 Wiley Periodicals, Inc.