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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/66133


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    题名: Extracting Informative Variables in the Validation of Two-group Causal Relationship
    作者: 洪英超
    Hung, Ying-chao;Tseng, Neng-fang
    贡献者: 統計系
    关键词: Causal relationship;Vector autoregression model;Informative variables;Modified Wald test;Automatic computer-search algorithm
    日期: 2013.06
    上传时间: 2014-05-21 17:32:10 (UTC+8)
    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
    關聯: Computational Statistics, 28(3), 1151-1167
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1007/s00180-012-0351-z
    DOI: 10.1007/s00180-012-0351-z
    显示于类别:[統計學系] 期刊論文

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