| dc.contributor | 統計系 | en_US |
| dc.creator (作者) | 洪英超 | zh_TW |
| dc.creator (作者) | Hung, Ying-chao; Tseng, Neng-fang | en_US |
| dc.date (日期) | 2013.06 | en_US |
| dc.date.accessioned | 21-May-2014 17:32:10 (UTC+8) | - |
| dc.date.available | 21-May-2014 17:32:10 (UTC+8) | - |
| dc.date.issued (上傳時間) | 21-May-2014 17:32:10 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/66133 | - |
| dc.description.abstract (摘要) | 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 | en_US |
| dc.format.extent | 146 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Computational Statistics, 28(3), 1151-1167 | en_US |
| dc.subject (關鍵詞) | Causal relationship;Vector autoregression model;Informative variables;Modified Wald test;Automatic computer-search algorithm | en_US |
| dc.title (題名) | Extracting Informative Variables in the Validation of Two-group Causal Relationship | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1007/s00180-012-0351-z | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1007/s00180-012-0351-z | en_US |
