ArticleEntropy (Basel, Switzerland)2020
Measuring Independence between Statistical Randomness Tests by Mutual Information.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Terrorism group prediction using feature combination and BiGRU with self-attention mechanism.PeerJ. Computer science · 2024Article
- Complexity Reduction in Analyzing Independence between Statistical Randomness Tests Using Mutual Information.Entropy (Basel, Switzerland) · 2023Article
- Analyses of unpredictable properties of a wind-driven triboelectric random number generator.Scientific reports · 2023Article
- Critical Analysis of Hypothesis Tests in Federal Information Processing Standard (140-2).Entropy (Basel, Switzerland) · 2022Article
- A strong construction of S-box using Mandelbrot set an image encryption scheme.PeerJ. Computer science · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of independence between statistical randomness tests has had great attention in the literature recently. Dependency detection between statistical randomness tests allows one to discriminate statistical randomness tests that measure similar characteristics, and thus minimize the amount of statistical randomness tests that need to be used. In this work, a method for detecting statistical dependency by using mutual information is proposed. The main advantage of using mutual information is its ability to detect nonlinear correlations, which cannot be detected by the linear correlation coefficient used in previous work. This method analyzes the correlation between the battery tests of the National Institute of Standards and Technology, used as a standard in the evaluation of randomness. The results of the experiments show the existence of statistical dependencies between the tests that have not been previously detected.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.