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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">218_OK</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.201801_16(1).0007</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Power Lomax Poisson Distribution: Properties and Estimation</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Hassan</surname>
            <given-names>Amal S.</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Department of Mathematical Statistics, Cairo University</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Nassr</surname>
            <given-names>Said G.</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">Faculty of Business Administration and International Marketing, Sinai University</aff>
      </contrib-group>
      <volume>16</volume>
      <issue>1</issue>
      <fpage>105</fpage>
      <lpage>128</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>A new four-parameter lifetime distribution named as the power Lomax Poisson is introduced and studied. The subject distribution is obtained by combining the power Lomax and Poisson distributions. Structural properties of the power Lomax Poisson model are implemented. Estimation of the model parameters are performed using the maximum likelihood, least squares and weighted least squares techniques. An intensive simulation study is performed for evaluating the performance of different estimators based on their relative biases, standard errors and mean square errors. Eventually, the superiority of the new compounding distribution over some existing distribution is illustrated by means of two real data sets. The results showed the fact that, the suggested model can produce better fits than some well-known distributions.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Power Lomax distribution</kwd>
        <kwd>Poisson distribution</kwd>
        <kwd>Probability Weighted moments</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
