A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Bias in O-Information Estimation
Tekijät: Gehlen, Johanna; Li, Jie; Hourican, Cillian; Tassi, Stavroula; Mishra, Pashupati P.; Lehtimäki, Terho; Kähönen, Mika; Raitakari, Olli; Bosch, Jos A.; Quax, Rick
Kustantaja: MDPI
Julkaisuvuosi: 2024
Journal: Entropy
Tietokannassa oleva lehden nimi: Entropy (Basel, Switzerland)
Lehden akronyymi: Entropy (Basel)
Artikkelin numero: 837
Vuosikerta: 26
Numero: 10
ISSN: 1099-4300
eISSN: 1099-4300
DOI: https://doi.org/10.3390/e26100837
Verkko-osoite: https://doi.org/10.3390/e26100837
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/458970411
Higher-order relationships are a central concept in the science of complex systems. A popular method of attempting to estimate the higher-order relationships of synergy and redundancy from data is through the O-information. It is an information-theoretic measure composed of Shannon entropy terms that quantifies the balance between redundancy and synergy in a system. However, bias is not yet taken into account in the estimation of the O-information of discrete variables. In this paper, we explain where this bias comes from and explore it for fully synergistic, fully redundant, and fully independent simulated systems of n=3 variables. Specifically, we explore how the sample size and number of bins affect the bias in the O-information estimation. The main finding is that the O-information of independent systems is severely biased towards synergy if the sample size is smaller than the number of jointly possible observations. This could mean that triplets identified as highly synergistic may in fact be close to independent. A bias approximation based on the Miller-Maddow method is derived for the O-information. We find that for systems of n=3 variables the bias approximation can partially correct for the bias. However, simulations of fully independent systems are still required as null models to provide a benchmark of the bias of the O-information.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
Johanna Gehlen, Jie Li, Stavroula Tassi, Jos A. Bosch, and Rick Quax were supported by the EU project To_Aition, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 848146. Cillian Hourican and Rick Quax were supported by the Netherlands Organisation for Health Research and Development (ZonMw), Open Competition Grant 09120012010063. The Young Finns Study has been financially supported by the Academy of Finland: grants 356405, 322098, 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117797 (Gendi), and 141071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; The Sigrid Juselius Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; Diabetes Research Foundation of Finnish Diabetes Association; EU Horizon 2020 (grant 755320 for TAXINOMISIS and grant 848146 for To Aition); European Research Council (grant 742927 for MULTIEPIGEN project); Tampere University Hospital Supporting Foundation; Finnish Society of Clinical Chemistry; the Cancer Foundation Finland; pBETTER4U_EU (Preventing obesity through Biologically and bEhaviorally Tailored inTERventions for you; project number: 101080117); and the Jane and Aatos Erkko Foundation. PPM was supported by the Academy of Finland (Grant number: 349708).
