We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Extracting informative variables in the validation of two-group causal relationship.
- Authors
Hung, Ying-Chao; Tseng, Neng-Fang
- 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.
- Subjects
MATHEMATICAL variables; GROUP theory; MULTIVARIATE analysis; TIME series analysis; DATA analysis; ALGORITHMS
- Publication
Computational Statistics, 2013, Vol 28, Issue 3, p1151
- ISSN
0943-4062
- Publication type
Article
- DOI
10.1007/s00180-012-0351-z