Abstract— Copula is introduced as a tool for understanding the dependence structure among random variables. Copulas make a link between multivariate joint distributions and univariate marginal distributions, and provide a flexible way to describe nonlinear dependence; copulas therefore have been applied in many fields. Here we deal with a family of generalized Archimedean (GA) copulas. In terms of these GA copulas, we derive the formula for the Kendall’s tau, which is a well known measure of concordance. Applications to the management are discussed.
Index Terms— Copula, (generalized) Archimedean copula, Kendall’s tau, information management.
N. Ishimura is with the Graduate School of Economics, Hitotsubashi University, Kunitachi, Tokyo 186-8601, Japan (e-mail: ishimura@econ.hit-u.ac.jp).
T. Li was with Hitotsubashi University. She is now with the Kokusai Asset Management Co. Ltd., Japan (e-mail: ri_ai_te@yahoo.com).
M. A. Nakamura is with the College of Science and Technology, Nihon University, Kanda-Surugadai, Tokyo 101-8308, Japan (e-mail: nakamura@math.cst.nihon-u.ac.jp).
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Cite: Naoyuki Ishimura, Ting Li, and Masa Aki Nakamura, " Copulas and the Information Management," International Journal of Information and Electronics Engineering vol. 4, no. 3, pp. 180-183, 2014.
