A new class of entropy estimators for multi-dimensional densities

Author

Miller, Erik G.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA

Volume

3

fYear

2003

fDate

6-10 April 2003

Abstract

We present a new class of estimators for approximating the entropy of multi-dimensional probability densities based on a sample of the density. These estimators extend the classic "m-spacing" estimators of Vasicek (1976) and others for estimating entropies of one-dimensional probability densities. Unlike plug-in estimators of entropy, which first estimate a probability density and then compute its entropy. our estimators avoid the difficult intermediate step of density estimation. For fixed dimension. the estimators an polynomial in the sample size. Similarities to consistent and asymptotically efficient one-dimensional estimators of entropy suggest that our estimators may sham these properties.

Keywords

entropy; parameter estimation; probability; statistical analysis; asymptotically efficient 1D estimators; continuous probability density; density estimation; entropy estimators; m-spacing estimators; multi-dimensional probability densities; one-dimensional probability densities; order statistics; plug-in estimators; polynomial estimators; sample size; Distributed computing; Entropy; Parameter estimation; Parametric statistics; Polynomials; Probability; Random variables; Statistical distributions; Testing; Zinc;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-7663-3

Type

conf

DOI

10.1109/ICASSP.2003.1199463

Filename

1199463