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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">120105</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.2014.12(1).1213
</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>L-Moments Estimations for Mixture of Weibull Distributions</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Erişoğlu</surname>
            <given-names>Ülkü</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Necmettin Erbakan University</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Erişoğlu</surname>
            <given-names>Murat</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">Necmettin Erbakan University</aff>
      </contrib-group>
      <volume>12</volume>
      <issue>1</issue>
      <fpage>69</fpage>
      <lpage>85</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: Mixture of Weibull distributions has wide application in modeling of heterogeneous data sets. The parameter estimation is one of the most important problems related to mixture of Weibull distributions. In this pa per, we propose a L-moment estimation method for mixture of two Weibull distributions. The proposed method is compared with maximum likelihood estimation (MLE) method according to the bias, the mean absolute error, the mean total error and completion time of the algorithm (time) by sim ulation study. Also, applications to real data sets are given to show the flexibility and potentiality of the proposed estimation method. The com parison shows that, the proposed method is better than MLE method.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>EM algorithm</kwd>
        <kwd>heterogeneous data</kwd>
        <kwd>L-moment</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
