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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">2017_4-4</article-id>
	  <article-id pub-id-type="doi">10.6339/JDS.201704_15(2).0004</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>The Marshall-Olkin Extended Generalized Gompertz Distribution</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Benkhelifa</surname>
            <given-names>Lazhar</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Laboratory of Applied Mathematics, Mohamed Khider University, Biskra, Algeria; 
Department of Mathematics and Informatics, Larbi Ben M'Hidi University, Oum El-Bouaghi, Algeria</aff>
      </contrib-group>
      <volume>15</volume>
      <issue>2</issue>
      <fpage>239</fpage>
      <lpage>266</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>A new four-parameter model called the Marshall-Olkin extended generalized Gompertz distribution is introduced. Its hazard rate function can be constant, increasing, decreasing, upside-down bathtub or bathtub-shaped depending on its parameters. Some mathematical properties of this model such as expansion for the density function, moments, moment generating function, quantile function, mean deviations, mean residual life, order statistics and Rényi entropy are derived. The maximum likelihood technique is used to estimate the unknown model parameters and the observed information matrix is determined. The applicability of the proposed model is shown by means of a real data set.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Marshall-Olkin distribution</kwd>
        <kwd>generalized Gompertz distribution, moments</kwd>
        <kwd>observed information matrix</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
