Extracting informative variables in the validation of two-group causal relationship |
| |
Authors: | Ying-Chao Hung Neng-Fang Tseng |
| |
Institution: | 1. Department of Statistics, National Chengchi University, NO. 64, Sec. 2, ZhiNan Rd., Wenshan District, Taipei ?, 11605, Taiwan 2. Department of Mathematical Statistics and Actuarial Science, Aletheia University, 32 Chen-Li Street, Tamsui, Taipei, 25103, Taiwan
|
| |
Abstract: | The validation of causal relationship between two groups of multivariate time series data often requires the precedence knowledge of all variables. However, in practice one finds that some variables may be negligible in describing the underlying causal structure. In this article we provide an explicit definition of “non-informative variables” in a two-group causal relationship and introduce various automatic computer-search algorithms that can be utilized to extract informative variables based on a hypothesis testing procedure. The result allows us to represent a simplified causal relationship by using minimum possible information on two groups of variables. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|