Given a compact set of real numbers, a random (C^ m + )-diffeomorphism is constructed such that the image of any measure concentrated on the set and satisfying a certain condition involving a real number (s ), almost surely has Fourier dimension greater than or equal to (s (m + ) ). This is used to show that every Borel subset of the real numbers of Hausdorff dimension (s ) is (C^ m + )-equivalent to a set of Fourier dimension greater than or equal to (s (m + ) ). In particular every Borel set is diffeomorphic to a Salem set, and the Fourier dimension is not invariant under (C^ m )-diffeomorphisms for any (m ).
In @cite_4 , Bluhm gave a method for randomly perturbing a class of self-similar measures on @math , such that the perturbed measure almost surely has Fourier dimension equal to the similarity dimension of the original measure. For @math , the uniform measures on Cantor sets with constant contraction ratio are among the measures considered by Bluhm, and if the parameters in the construction are chosen suitably then the perturbation is a bi-Lipschitz map. Thus it follows from Bluhm's result that such Cantor sets are bi-Lipschitz equivalent to Salem sets.
@cite_4
2021822733 2127184781 2562674776 2962804622
In this paper we investigate the pointwise Fourier decay of some selfsimilar random measures. As an application we construct statistically selfsimilar Salem sets. For example, our result shows that a “slight” random perturbation of the classical Cantor set becomes a “nice” set in the sense that its Fourier dimension equals its Hausdorff dimension. We consider the non-parametric estimation of a function that is observed in white noise after convolution with a boxcar, the indicator of an interval ( a; a). In a recent paper Johnstone, Kerkyacharian, Picard and Raimondo (2004) have developed a wavelet deconvolution method (called WaveD) that can be used for " boxcar kernels. For example, WaveD can be tuned to achieve near optimal rates over Besov spaces when a is a Badly Approximable (BA) irrational number. While the set of all BA's contains quadratic irrationals, e.g., a = p 5, it has Lebesgue measure zero. In this paper we derive two tuning scenarios of WaveD that are valid for all" boxcar convolutions (i.e., when a 2 A where A is a full Lebesgue measure set). We propose (i) a tuning inspired from Minimax theory over Besov spaces; (ii) a tuning derived from Maxiset theory providing similar rates as for WaveD in the BA widths setting. Asymptotic theory nds that (i) in the worst case scenario, departures from the BA assumption aect WaveD convergence rates, at most, by log factors; (ii) the Maxiset tuning, which yields smaller thresholds, is superior to the Minimax tuning over a whole range of Besov sub-scales. Our asymptotic results are illustrated in an extensive simulation of boxcar convolution observed in white noise. We consider in this paper the problem of sampling a high-dimensional probability distribution @math having a density with respect to the Lebesgue measure on @math , known up to a normalization constant @math . Such problem naturally occurs for example in Bayesian inference and machine learning. Under the assumption that @math is continuously differentiable, @math is globally Lipschitz and @math is strongly convex, we obtain non-asymptotic bounds for the convergence to stationarity in Wasserstein distance of order @math and total variation distance of the sampling method based on the Euler discretization of the Langevin stochastic differential equation, for both constant and decreasing step sizes. The dependence on the dimension of the state space of these bounds is explicit. The convergence of an appropriately weighted empirical measure is also investigated and bounds for the mean square error and exponential deviation inequality are reported for functions which are measurable and bounded. An illustration to Bayesian inference for binary regression is presented to support our claims. We consider the perturbation of elliptic pseudodifferential operators @math with more than square integrable Green's functions by random, rapidly varying, sufficiently mixing, potentials of the form @math . We analyze the source and spectral problems associated with such operators and show that the rescaled difference between the perturbed and unperturbed solutions may be written asymptotically as @math as explicit Gaussian processes. Such results may be seen as central limit corrections to homogenization (law of large numbers). Similar results are derived for more general elliptic equations with random coefficients in one dimension of space. The results are based on the availability of a rapidly converging integral formulation for the perturbed solutions and on the use of classical central limit results for random processes with appropriate mixing conditions.