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oai:docta.ucm.es:20.500.14352/716902024-07-19T14:24:42Zcom_20.500.14352_14col_20.500.14352_15

Almaraz Luengo, Elena Salome Leiva Cerna, Marcos Brian García Villalba, Luis Javier Hernández Castro, Julio 2023-06-22T10:48:01Z 2023-06-22T10:48:01Z 2022-04-02 Almaraz Luengo, E. S:, Leiva Cerna, M. B., García Villalba, L. J. & Hernández Castro, J. «A New Approach to Analyze the Independence of Statistical Tests of Randomness». Applied Mathematics and Computation, vol. 426, agosto de 2022, p. 127116. DOI.org (Crossref), https://doi.org/10.1016/j.amc.2022.127116. 0096-3003 10.1016/j.amc.2022.127116 https://hdl.handle.net/20.500.14352/71690 https://doi.org/10.1016/j.amc.2022.127116 https://www.sciencedirect.com/science/article/pii/S0096300322002004 One of the fundamental aspects when working with batteries of statistic tests is that they should be as efficient as possible, i.e. that they should check the properties and do so in a reasonable computational time. This assumes that there are no tests that are checking the same properties, i.e. that they are not correlated. One of the most commonly used measures to verify the interrelation between variables is the Pearson’s correlation. In this case, linear dependencies are checked, but it may be interesting to verify other types of non-linear relationships between variables. For this purpose, mutual information has recently been proposed, which measures how much information, on average, one random variable provides to another. In this work we analyze some well-known batteries by using correlation analysis and mutual information approaches. eng https://creativecommons.org/licenses/by-nc-nd/3.0/es/ open access Atribución-NoComercial-SinDerivadas 3.0 España A new approach to analyze the independence of statistical tests of randomness journal article