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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
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/66133

    Title: Extracting Informative Variables in the Validation of Two-group Causal Relationship
    Authors: 洪英超
    Hung, Ying-chao;Tseng, Neng-fang
    Contributors: 統計系
    Keywords: Causal relationship;Vector autoregression model;Informative variables;Modified Wald test;Automatic computer-search algorithm
    Date: 2013.06
    Issue Date: 2014-05-21 17:32:10 (UTC+8)
    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
    Relation: Computational Statistics, 28(3), 1151-1167
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1007/s00180-012-0351-z
    DOI: 10.1007/s00180-012-0351-z
    Appears in Collections:[統計學系] 期刊論文

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