Transfer Entropy Expressions for a Class of Non-Gaussian Distributions

Entropy

Mehrdad Jafari-Mamaghani¹ 

[1] Department of Mathematics, Stockholm University, SE-106 91 Stockholm, Sweden; E-Mail:

关键词: Granger causality;    information theory;    transfer entropy;    multivariate distributions;    power-law distributions;   

DOI  :  10.3390/e16031743

来源: mdpi

【 摘 要 】

Transfer entropy is a frequently employed measure of conditional co-dependence in non-parametric analysis of Granger causality. In this paper, we derive analytical expressions for transfer entropy for the multivariate exponential, logistic, Pareto (type ℐ – ℐ) and Burr distributions. The latter two fall into the class of fat-tailed distributions with power law properties, used frequently in biological, physical and actuarial sciences. We discover that the transfer entropy expressions for all four distributions are identical and depend merely on the multivariate distribution parameter and the number of distribution dimensions. Moreover, we find that in all four cases the transfer entropies are given by the same decreasing function of distribution dimensionality.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland