Gaussian bounds for derivatives of central Gaussian semigroups on compact groups

Hardy Spaces for Semigroups with Gaussian Bounds

Let Tt = e−tL be a semigroup of self-adjoint linear operators acting on L(X,μ), where (X, d, μ) is a space of homogeneous type. We assume that Tt has an integral kernel Tt(x, y) which satisfies the upper and lower Gaussian bounds: C1 μ(B(x, √ t)) exp ( −c1d(x, y)/t ) ≤ Tt(x, y) ≤ C2 μ(B(x, √ t)) exp ( −c2d(x, y)/t ) . By definition, f belongs to H L if ‖f‖H1 L = ‖ supt>0 |Ttf(x)|‖L1(X,μ) < ∞. W...

Mock–gaussian Behaviour for Linear Statistics of Classical Compact Groups

We consider the scaling limit of linear statistics for eigenphases of a matrix taken from one of the classical compact groups. We compute their moments and find that the first few moments are Gaussian, whereas the limiting distribution is not. The precise number of Gaussian moments depends upon the particular statistic considered.

Central Gaussian semigroups of measures with continuous density

Moment bounds and central limit theorems for Gaussian subordinated arrays

A general moment bound for sums of products of Gaussian vector’s functions extending the moment bound in Taqqu (1977, Lemma 4.5) is established. A general central limit theorem for triangular arrays of nonlinear functionals of multidimensional non-stationary Gaussian sequences is proved. This theorem extends the previous results of Breuer and Major (1981), Arcones (1994) and others. A Berry-Ess...

Information bounds for Gaussian copulas.

Often of primary interest in the analysis of multivariate data are the copula parameters describing the dependence among the variables, rather than the univariate marginal distributions. Since the ranks of a multivariate dataset are invariant to changes in the univariate marginal distributions, rank-based estimators are natural candidates for semiparametric copula estimation. Asymptotic informa...