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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JDS</journal-id>
      <journal-title-group>
        <journal-title>Journal of Data Science</journal-title>
      </journal-title-group>
      <issn pub-type="epub">1680-743X</issn>
      <issn pub-type="ppub">1680-743X</issn>
      <publisher>
        <publisher-name>SOSRUC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">120305</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.201407_12(3).0005</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Copula-based Logistic Regression Models for Bivariate Binary Responses</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Li</surname>
            <given-names>Xiaohu</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">University of New Orleans, Xiamen University</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Li</surname>
            <given-names>Linxiong</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">University of New Orleans</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Fang</surname>
            <given-names>Rui</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_002"/>
        </contrib>
        <aff id="j_JDS_aff_002">Xiamen University</aff>
      </contrib-group>
      <volume>12</volume>
      <issue>3</issue>
      <fpage>461</fpage>
      <lpage>476</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: The association between bivariate binary responses has been studied using Pearson’s correlation coefficient, odds ratio, and tetrachoric correlation coefficient. This paper introduces a copula to model the association. Numerical comparisons between the proposed method and the existing methods are presented. Results show that these methods are comparative. However, the copula method has a clearer interpretation and is easier to extend to bivariate responses with three or more ordinal categories. In addition, a goodness-of-fit test for the selection of a model is performed. Applications of the method on two real data sets are also presented.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Clayton copula</kwd>
        <kwd>Frank copula</kwd>
        <kwd>Maximum likelihood estimation</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
