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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">090408</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.201110_09(4).0008</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>To Do or Not To Do Business with a Country: A Robust Classification Approach</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Bhattacharyya</surname>
            <given-names>Kuntal</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Indiana State University</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Datta</surname>
            <given-names>Pratim</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">Kent State University</aff>
      </contrib-group>
      <volume>9</volume>
      <issue>4</issue>
      <fpage>607</fpage>
      <lpage>623</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: In the face of global uncertainty and a growing reliance on third party indices to gain a snapshot of a country’s operations, accurate decision making makes or breaks relationships in global trade. Under this aegis, we question the validity of traditional logistic regression using the maximum likelihood estimator (MLE) in classifying countries for doing business. This paper proposes that a weighted version of the Bianco and Yohai (BY) estimator is a superlative and robust (outlier resistant) tool in the hands of practitioners to gauge the correct antecedents of a country’s internal environment and decide whether to do or not do business with that country. In addition, this robust process is effective in differentiating between “problem” countries and “safe” countries for doing business. An existing “R” program for the BY estimation technique by Croux and Haesbroeck has been modified to fit our cause.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Global supply chain</kwd>
        <kwd>outlier management</kwd>
        <kwd>country risk</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
