Approximation of Projections of Random Vectors

  title={Approximation of Projections of Random Vectors},
  author={Elizabeth S. Meckes},
  journal={Journal of Theoretical Probability},
Let X be a d-dimensional random vector and Xθ its projection onto the span of a set of orthonormal vectors {θ1,…,θk}. Conditions on the distribution of X are given such that if θ is chosen according to Haar measure on the Stiefel manifold, the bounded-Lipschitz distance from Xθ to a Gaussian distribution is concentrated at its expectation; furthermore, an explicit bound is given for the expected distance, in terms of d, k, and the distribution of X, allowing consideration not just of fixed k… 
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  • G. Reeves
  • Computer Science, Mathematics
    2017 IEEE International Symposium on Information Theory (ISIT)
  • 2017
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