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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">189</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.201707_15(3).0003</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>The Generalized Marshall-Olkin-Kumaraswamy-G Family of Distributions</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Chakraborty</surname>
            <given-names>Subrata</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Department of Statistics, Dibrugarh University, Dibrugarh-786004, Assam, India</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Handique</surname>
            <given-names>Laba</given-names>
          </name>
        </contrib>
      </contrib-group>
      <volume>15</volume>
      <issue>3</issue>
      <fpage>391</fpage>
      <lpage>422</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: A family of distribution is proposed by using Kumaraswamy-G ( Kw − G ) distribution as the base line distribution in the generalized Marshall-Olkin (GMO) construction. By expanding the probability density function and the survival function as infinite series the proposed family is seen as infinite mixtures of the Kw − G distribution. Series expansions of the density function for order statistics are also obtained. Moments, moment generating function, Rényi entropy, quantile function, random sample generation, asymptotes, shapes and stochastic orderings are also investigated. Maximum likelihood estimation, their large sample standard error, confidence intervals and method of moment are presented. Three real life illustrations of comparative data modeling applications with some of the important sub mode</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Marshall - Olkin -Kumaraswamy-G family</kwd>
        <kwd>Generalized Marshall-Olkin family</kwd>
        <kwd>Exponentiated family</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
