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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">040201</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.2006.04(2).266
</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>A Bayesian Approach to the Multiple Comparisons Problem</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Neath</surname>
            <given-names>Andrew A.</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Southern Illinois University</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Cavanaugh</surname>
            <given-names>Joseph E.</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">University of Iowa</aff>
      </contrib-group>
      <volume>4</volume>
      <issue>2</issue>
      <fpage>131</fpage>
      <lpage>146</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: Consider the problem of selecting independent samples from several populations for the purpose of between-group comparisons. An important aspect of the solution is the determination of clusters where mean levels are equal, often accomplished using multiple comparisons testing. We formulate the hypothesis testing problem of determining equal-mean clusters as a model selection problem. Information from all competing models is combined through Bayesian methods in an effort to provide a more realistic accounting of uncertainty. An example illustrates how the Bayesian approach leads to a logically sound presentation of multiple comparison results.</p>
      </abstract>
    </article-meta>
  </front>
</article>
