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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">070402</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.2009.07(4).443
</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>The Log-exponentiated-Weibull Regression Models with Cure Rate: Local Influence and Residual Analysis</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Cancho</surname>
            <given-names>Vicente G.</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">University of S˜ao Paulo</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Ortega</surname>
            <given-names>Edwin M. M.</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">University of S˜ao Paulo</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Bolfarine</surname>
            <given-names>Heleno</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_002"/>
        </contrib>
        <aff id="j_JDS_aff_002">University of S˜ao Paulo</aff>
      </contrib-group>
      <volume>7</volume>
      <issue>4</issue>
      <fpage>433</fpage>
      <lpage>458</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: In this paper the log-exponentiated-Weibull regression model is modified to allow the possibility that long term survivors are present in the data. The modification leads to a log-exponentiated-Weibull regression model with cure rate, encompassing as special cases the log-exponencial regression and log-Weibull regression models with cure rate typically used to model such data. The models attempt to estimate simultaneously the effects of covariates on the acceleration/deceleration of the timing of a given event and the surviving fraction; that is, the proportion of the population for which the event never occurs. Assuming censored data, we consider a classic analysis and Bayesian analysis for the parameters of the proposed model. The normal curvatures of local influence are derived under various perturbation schemes and two deviance-type residuals are proposed to assess departures from the log-exponentiated-Weibull error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed.</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Cure rate models</kwd>
        <kwd>exponentiated-Weibull distribution</kwd>
        <kwd>residual analysis</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
