Nonparametric estimation of information-based measures of statistical dispersion

KOŠTÁL, Lubomír and Ondřej POKORA. Nonparametric estimation of information-based measures of statistical dispersion. Entropy. 2012, vol. 14, No 7, p. 1221–1233. ISSN 1099-4300. Available from: https://dx.doi.org/10.3390/e14071221.

Basic information

Original name

Nonparametric estimation of information-based measures of statistical dispersion

Authors

KOŠTÁL, Lubomír and Ondřej POKORA

Edition

Entropy, 2012, 1099-4300

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Other information

Language

English

Type of outcome

Article in a journal

Field of Study

10103 Statistics and probability

Country of publisher

Switzerland

Confidentiality degree

is not subject to a state or trade secret

Impact factor

Impact factor: 1.347

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.3390/e14071221

UT WoS

000306748500007

Keywords in English

statistical dispersion; entropy; Fisher information; nonparametric density estimation

Tags

International impact, Reviewed

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Abstract

V originále

We address the problem of non-parametric estimation of the recently proposed measures of statistical dispersion of positive continuous random variables. The measures are based on the concepts of differential entropy and Fisher information and describe the "spread" or "variability" of the random variable from a different point of view than the ubiquitously used concept of standard deviation. The maximum penalized likelihood estimation of the probability density function proposed by Good and Gaskins is applied and a complete methodology of how to estimate the dispersion measures with a single algorithm is presented. We illustrate the approach on three standard statistical models describing neuronal activity.