Explicit rates of approximation in the CLT for quadratic forms
Abstract
Let X,X1,X2,… be i.i.d. Rd-valued real random vectors. Assume that EX=0, covX=C, E X2=σ 2 and that X is not concentrated in a proper subspace of Rd. Let G be a mean zero Gaussian random vector with the same covariance operator as that of X. We study the distributions of nondegenerate quadratic forms Q[SN] of the normalized sums SN=N-1/2(X1+·s+XN) and show that, without any additional conditions, \[Ndef=x |P\Q[SN]≤ x\-P\Q[G]≤ x\|=O(N-1),\] provided that d≥5 and the fourth moment of X exists. Furthermore, we provide explicit bounds of order O(N-1) for N for the rate of approximation by short asymptotic expansions and for the concentration functions of the random variables Q[SN+a], a∈Rd. The order of the bound is optimal. It extends previous results of Bentkus and G\"otze [Probab. Theory Related Fields 109 (1997a) 367-416] (for d9) to the case d5, which is the smallest possible dimension for such a bound. Moreover, we show that, in the finite dimensional case and for isometric Q, the implied constant in O(N-1) has the form cdσ d()-1/2 E\|C-1/2X\|4 with some cd depending on d only. This answers a long standing question about optimal rates in the central limit theorem for quadratic forms starting with a seminal paper by Ess\'een [Acta Math. 77 (1945) 1-125].