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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">110408</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.2013.11(4).1207
</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>A Bayesian Analysis of the Spherical Distribution in Presence of Covariates</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Achcar</surname>
            <given-names>Jorge Alberto</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">University of S˜ao Paulo</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Napa</surname>
            <given-names>Gian Franco</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">University of S˜ao Paulo</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Souza</surname>
            <given-names>Roberto Molina De</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_002"/>
        </contrib>
        <aff id="j_JDS_aff_002">Federal Technological University of Paran´a</aff>
      </contrib-group>
      <volume>11</volume>
      <issue>4</issue>
      <fpage>819</fpage>
      <lpage>833</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: In this paper we introduce a Bayesian analysis of a spherical distri bution applied to rock joint orientation data in presence or not of a vector of covariates, where the response variable is given by the angle from the mean and the covariates are the components of the normal upwards vector. Standard simulation MCMC (Markov Chain Monte Carlo) methods have been used to obtain the posterior summaries of interest obtained from Win Bugs software. Illustration of the proposed methodology are given using a simulated data set and a real rock spherical data set from a hydroelectrical site.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Bayesian analysis</kwd>
        <kwd>MCMC methods</kwd>
        <kwd>regression models</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
