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Barankin Vector Locally Best Unbiased Estimates

Machine Learning 2017-07-03 v1 Information Theory math.IT Probability

Abstract

The Barankin bound is generalized to the vector case in the mean square error sense. Necessary and sufficient conditions are obtained to achieve the lower bound. To obtain the result, a simple finite dimensional real vector valued generalization of the Riesz representation theorem for Hilbert spaces is given. The bound has the form of a linear matrix inequality where the covariances of any unbiased estimator, if these exist, are lower bounded by matrices depending only on the parametrized probability distributions.

Keywords

Cite

@article{arxiv.1706.10062,
  title  = {Barankin Vector Locally Best Unbiased Estimates},
  author = {Bruno Cernuschi-Frias},
  journal= {arXiv preprint arXiv:1706.10062},
  year   = {2017}
}

Comments

29 pages

R2 v1 2026-06-22T20:34:12.894Z