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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_4-2</article-id>
	  <article-id pub-id-type="doi">10.6339/JDS.201704_15(2).0002</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>On the Extension of Inverse Lindley Distribution</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Sharma</surname>
            <given-names>Vikas Kumar</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Department of Mathematics, Institute of Infrastructure Technology, Research and Management (IITRAM), Maninagar East, Ahmedabad, India</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Khandelwal</surname>
            <given-names>Pragya</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">Central University of Rajasthan, Bandersindri, Ajmer, India</aff>
      </contrib-group>
      <volume>15</volume>
      <issue>2</issue>
      <fpage>205</fpage>
      <lpage>220</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>In this paper, we proposed another extension of inverse Lindley distribution, called extended inverse Lindley and studied its fundamental properties such as moments, inverse moments, mean deviation, stochastic ordering and entropy. The flexibility of the proposed distribution is shown by studying monotonicity properties of density and hazard functions. It is shown that the distribution belongs to the family of upside-down bathtub shaped distributions. Maximum likelihood estimators along with asymptotic confidence intervals are constructed for estimating the unknown parameters. An algorithm is presented for random number generation form the distribution. The property of consistency of MLEs has been verified on the basis of simulated samples. The applicability of the extended inverse Lindley distribution is illustrated by means of real data analysis.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Extended inverse Lindley distribution</kwd>
        <kwd>upside-down bathtub shaped hazard rate</kwd>
        <kwd>Moments</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
