@Article{RamosCasaMaca:2020:InHiAp,
               author = "Ramos, Ant{\^o}nio M{\'a}rio de torres and Casagrande, Helder 
                         Luciani and Macau, Elbert Einstein Nehrer",
          affiliation = "{Ita{\'u} Asset Management} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Investigation on the high-order approximation of the entropy 
                         bias",
              journal = "Physica A: Statistical Mechanics and its Applications",
                 year = "2020",
               volume = "549",
                pages = "e124301",
                month = "July",
             keywords = "Entropy bias, Mutual information, Multiple comparison analysis, 
                         Complex network.",
             abstract = "The estimation of entropy from experimental data has a 
                         considerable bias when the discretization of the variable domain 
                         is comparable to the sample size. In this case, the source of the 
                         bias is the difference between the a priori distribution and the 
                         observed distribution from sampled data. In this paper, we 
                         estimate the entropy bias considering an infinite sum of central 
                         moments of the binomial distribution using two probability mass 
                         functions. We analyze the bias in the light of the ratio between 
                         the number of the partition of the domain and the sample size. The 
                         main motivation of this study is improving statistical hypothesis 
                         testing in which probabilities are conceived beforehand. We 
                         examine the adequacy of high-order approximation according to the 
                         ratio between the sample size and the number of domain partitions. 
                         Finally, we expand the analysis to the entropy-derived mutual 
                         information and present an application for network 
                         reconstruction.",
                  doi = "10.1016/j.physa.2020.124301",
                  url = "http://dx.doi.org/10.1016/j.physa.2020.124301",
                 issn = "0378-4371",
             language = "en",
           targetfile = "ramos_investigation.pdf",
        urlaccessdate = "27 abr. 2024"
}