Article ID Journal Published Year Pages File Type
5129532 Journal of Statistical Planning and Inference 2017 25 Pages PDF
Abstract

•We study in detail the bias and variance of the entropy estimator proposed by Kozachenko and Leonenko (1987) for a large class of densities on Rd.•We then use the work of Bickel and Breiman (1983) to prove a central limit theorem in dimensions 1 and 2.•In higher dimensions, we provide a development of the bias in terms of powers of N−2/d.•This allows us to use a Richardson extrapolation to build, in any dimension, a root-n consistent entropy estimator satisfying a central limit theorem which allows for explicit (asymptotic) confidence intervals.•To our knowledge, all the previous general root-n consistency results were concerning dimension 1.

We study in detail the bias and variance of the entropy estimator proposed by Kozachenko and Leonenko (1987) for a large class of densities on Rd. We then use the work of Bickel and Breiman (1983) to prove a central limit theorem in dimensions 1 and 2. In higher dimensions, we provide a development of the bias in terms of powers of N−2/d. This allows us to use a Richardson extrapolation to build, in any dimension, a root-n consistent entropy estimator satisfying a central limit theorem which allows for explicit (asymptotic) confidence intervals. To our knowledge, all the previous general root-n consistency results were concerning dimension 1.

Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
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