New Entropy Estimators with Smaller Root Mean Squared Error

Al-Omari, Amer Ibrahim

doi:10.22237/jmasm/1446350940

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…As mentioned before, it is well known that there is not an unbiased estimator [ 4 ] and of minimum variance, while the convergence rate of a consistent estimators can be arbitrarily slow [ 5 ]. Two well-known statistical tools of estimation theory will be used to experimentally evaluate the selected estimators, bias and mean square error [ 48 ]. The bias

of an estimator

measures the deviation of the estimate with respect to the expected real value and is calculated from the difference

, where

is the expected value of

In our scenario, we will work with uniformly distributed samples of bytes and bits so the data follows a uniform distribution and we have that

.…”

Section: Preliminariesmentioning

confidence: 99%

Selecting an Effective Entropy Estimator for Short Sequences of Bits and Bytes with Maximum Entropy

Rodríguez

Madarro-Capó

Legón-Pérez

et al. 2021

Entropy

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Entropy makes it possible to measure the uncertainty about an information source from the distribution of its output symbols. It is known that the maximum Shannon’s entropy of a discrete source of information is reached when its symbols follow a Uniform distribution. In cryptography, these sources have great applications since they allow for the highest security standards to be reached. In this work, the most effective estimator is selected to estimate entropy in short samples of bytes and bits with maximum entropy. For this, 18 estimators were compared. Results concerning the comparisons published in the literature between these estimators are discussed. The most suitable estimator is determined experimentally, based on its bias, the mean square error short samples of bytes and bits.

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