The logarithmic law of random determinant

preprint2015arXivOpen access

Abstract

Consider the square random matrix An=(aij)n,nA_n=(a_{ij})_{n,n}, where {aij:=aij(n),i,j=1,…,n}\{a_{ij}:=a_{ij}^{(n)},i,j=1,\ldots,n\} is a collection of independent real random variables with means zero and variances one. Under the additional moment condition sup⁡nmax⁡1≤i,j≤nEaij4<∞,\sup_n\max_{1\leq i,j\leq n}\mathbb{E}a_{ij}^4<\infty, we prove Girko's logarithmic law of det⁡An\det A_n in the sense that as n→∞n\rightarrow\infty \begin{eqnarray*}\frac{\log|\det A_n|-(1/2)\log(n-1)!}{\sqrt{(1/2)\log n}}\stackrel{d}{ \longrightarrow}N(0,1).\end{eqnarray*}

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