EFFICIENT MULTIVARIATE ENTROPY ESTIMATION VIA k-NEAREST NEIGHBOUR DISTANCES
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Many statistical procedures, including goodness-of-fit tests and methods for
independent component analysis, rely critically on the estimation of the
entropy of a distribution. In this paper, we seek entropy estimators that are
efficient and achieve the local asymptotic minimax lower bound with respect to
squared error loss. To this end, we study weighted averages of the estimators
originally proposed by Kozachenko and Leonenko (1987), based on the
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Leverhulme Trust (PLP-2014-353)
Engineering and Physical Sciences Research Council (EP/P031447/1)