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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">1708</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.201901_17(1).0008</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Weighted Quantile Regression Theory And Its Application Weibull Distribution As An Actuarial Risk Model: Computation Of Its Probability Of Ultimate Ruin And The Moments Of The Time To Ruin, Deficit At Ruin And Surplus Prior To Ruin</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Das</surname>
            <given-names>Jagriti</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Dept Of Statistics, Gauhati University, Assam, India</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Nath</surname>
            <given-names>Dilip C.</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">Administration, Assam University, Assam</aff>
      </contrib-group>
      <volume>17</volume>
      <issue>1</issue>
      <fpage>161</fpage>
      <lpage>194</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>The Weibull distribution due to its suitability to adequately model data with high degree of positive skewness which is a typical characteristics of the claim amounts, is considered a versatile model for loss modeling in general Insurance. In this paper, the Weibull distribution is fitted to a set of insurance claim data and the probability of ultimate ruin has been computed for Weibull distributed claim data using two methods, namely the Fast Fourier Transform and the 4 moment Gamma De Vylder approximation. The consistency has been found in the values obtained from the both the methods. For the same model, the first two moments of the time to ruin, deficit at the time of ruin and the surplus just prior to ruin have been computed numerically. The moments are found to be exhibiting behavior consistent to what is expected in practical scenario. The influence of the surplus process being subjected to the force of interest earnings and tax payments on the probability of ultimate ruin, causes the later to be higher than what is obtained in the absence of these factors.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Classical risk model</kwd>
        <kwd>Numerical integration</kwd>
        <kwd>Surplus process</kwd>
        <kwd>Time to ruin</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
